Studying spotify track data we are looking for the main features and extract the most valuable information of it, in order to understand which features make a song popular. We'll do that by:
The main objective of this project is to find the best model to classify a song by popularity (in ten popularity levels) given the characteristics of the song in order to understand what make a song popular and predict how popular a song can be. To do this, a supervised classification model will be estimated using the data from Lending Club Loan Data. The type of models to be compared are Logistic Regression, Support Vector Machine, Random Forest and XGBOOST. Additionaly, two new models will be tested: NGBoost and CatBoost.
The best model will be the one that outperforms the rest in the following scores:
- Accuracy: $$ \text { Accuracy }=\frac{t p + t n}{t p+f p+t n+f n } $$
- Precision: also called positive predictive value, is the fraction of relevant instances among the retrieved instances. $$ \text { Precision }=\frac{t p}{t p+f p} $$ - Recall: also known as sensitivity, is the fraction of the total amount of relevant instances that were actually retrieved. $$ \text { Recall }=\frac{t p}{t p+f n} $$
- F1-score: In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test's accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the number of all relevant samples (all samples that should have been identified as positive). The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.
$$ F_{1}=\left(\frac{2}{\mathrm{recall}^{-1}+\text { precision }^{-1}}\right)=2 \cdot \frac{\text { p } \cdot \text { r }}{\text { p }+\text { r }} $$Based on the proposed scores, the best model to predict the test set was the CatBoost Model; NGBoost had the best precision, but in accuracy, recall and f1-score, the best model is CatBoost. Because the classification is not dichotomous but multiclass, there is no graphical representation of the ROC curve due to dimensionality; In addition, it is not so easy to relate prediction metrics (for example, accuracy) to an established benchmark, the best benchmark for comparison is that it has 100% successes in some class and zero in the others (which on average equals a distribution uniform data by class) which in this case corresponds to 10% (due to the ten classes) [16.6% for 6 classes]
| Accuracy | precision | recall | f1-score | |
|---|---|---|---|---|
| Logistic Regression | 0.36 | 0.31 | 0.36 | 0.27 |
| SVM | 0.35 | 0.22 | 0.35 | 0.25 |
| SVM Polynomial | 0.37 | 0.42 | 0.37 | 0.29 |
| SVM RBF | 0.37 | 0.41 | 0.37 | 0.29 |
| SVM Sigmoid | 0.26 | 0.27 | 0.26 | 0.26 |
| Random Forest | 0.35 | 0.34 | 0.35 | 0.3 |
| XGBoost | 0.37 | 0.41 | 0.37 | 0.29 |
| CatBoost | 0.38 | 0.4 | 0.38 | 0.31 |
| NGBoost | 0.37 | 0.43 | 0.37 | 0.28 |
Looking at the feature importance for the models, every model had different features as the main characteristics to decide the classification, but some interesting conclusions arose from this initial approach.
| Feature Importance | Logistic Regression | SVM | Random Forest | XGBoost | CatBoost | NGBoost |
|---|---|---|---|---|---|---|
| 1 | danceability | loudness | duration_ms | duration_ms | duration_ms | duration_ms |
| 2 | instrumentalness | valence | loudness | loudness | danceability | loudness |
| 3 | explicit | time_signature | danceability | danceability | loudness | danceability |
| 4 | energy | explicit | valence | valence | energy | valence |
| 5 | valence | acousticness | acousticness | acousticness | speechiness | acousticness |
| 6 | mode | duration_ms | speechiness | speechiness | valence | speechiness |
| 7 | liveness | key | tempo | tempo | liveness | tempo |
| 8 | acousticness | instrumentalness | energy | energy | acousticness | energy |
| 9 | speechiness | danceability | liveness | liveness | instrumentalness | liveness |
| 10 | duration_ms | mode | instrumentalness | instrumentalness | tempo | instrumentalness |
| 11 | time_signature | tempo | key | key | key | key |
| 12 | tempo | speechiness | mode | mode | explicit | mode |
| 13 | loudness | energy | time_signature | time_signature | mode | time_signature |
| 14 | key | liveness | explicit | explicit | time_signature | explicit |
XGBOOST and Random Forest models selected a group of features as the most important; duration_ms, loudness, danceability, valence, acousticness, among other. Every feature for these models is related somehow to the rest of the features; happening in both models. On the other hand, the SVM Linear Kernel and the Logistic Regression (both linear) selected a group of features as the most important as well; explicit is the main feature diference between the two groups of models. The only variable shared by the groups is danceability. Apparently, there are two approaches for this classification problem: a linear approach and a non-linear; only by looking at the features each model selects.
Therefore, following what the best model says, if you want a song to reach the maximum level of popularity you must take into account the duration of the song; how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity; the energy it produces, how fast, loud and noisy it is; and level of decibels.
#spotify api
import spotipy
#dataframes
import pandas as pd
#scaling
from sklearn.preprocessing import MinMaxScaler
from spotipy.oauth2 import SpotifyClientCredentials
#Measure time
import time
#lasso
from sklearn import linear_model
from sklearn import datasets
from sklearn.svm import l1_min_c
#maths
import numpy as np
#visualization
import seaborn as sns
import graphviz
import panel
from matplotlib import pyplot as plt
#seed
import random
#Grid search
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
# logisticRegression
from sklearn.linear_model import LogisticRegression
import statsmodels.api as sm
#svm
from sklearn import svm
from sklearn import datasets, metrics, model_selection, svm
#Random Forest
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import export_graphviz
from subprocess import call
# Display in jupyter notebookfrom IPython.display import ImageImage(filename = 'tree.png')
import os
import six
from sklearn import tree
#xgboost
import xgboost as xgb
from xgboost import XGBClassifier
# catboost and ngboost
import catboost
import ngboost
from catboost import Pool, CatBoostClassifier
#performance metrics
from sklearn.metrics import classification_report, confusion_matrix, make_scorer, precision_score, recall_score, accuracy_score
# confusion matrix
from sklearn import metrics
Based on simple REST principles, the Spotify Web API endpoints return JSON metadata about music artists, albums, and tracks, directly from the Spotify Data Catalogue. Web API also provides access to user related data, like playlists and music that the user saves in the Your Music library. Such access is enabled through selective authorization, by the user. The data obtain through this API is limited and the databases may only vary in number and year when data is not from a users playlist.
Spotipy is a lightweight Python library for the Spotify Web API. With Spotipy you get full access to all of the music data provided by the Spotify platform. Spotipy supports all of the features of the Spotify Web API including access to all end points, and support for user authorization.
spotipy supports two authorization flows:
# CONNECT TO SPOTIFY
cid ="b43c7befbf6a4de29308d2dd70056f20" #get these from spotify for developers website
secret = "518b76d242ba4a07b37b735ec6ab1393"
client_credentials_manager = SpotifyClientCredentials(client_id=cid, client_secret=secret)
sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)
def get_playlist_tracks(username,playlist_id):
results = sp.user_playlist_tracks(username,playlist_id)
tracks = results['items']
while results['next']:
results = sp.next(results)
tracks.extend(results['items'])
return tracks
#GET SONG METRICS FROM PLAYLIST
userid = 'gjordj'
playlistURI = "1z6vJLmcITe0emKZQecTJC"
#connect to playlist
# playlist = sp.user_playlist(userid, playlistURI)
playlist = get_playlist_tracks(userid, playlistURI)
feature_list = []
reduce_sample = 2
#obtain data
for i in range(round(len(playlist)/reduce_sample)):
feature_list.append(sp.audio_features(playlist[i]['track']['id'])[0])
retrying ...1secs retrying ...1secs
dfmet = pd.DataFrame(feature_list)
dfmet.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1171 entries, 0 to 1170 Data columns (total 18 columns): acousticness 1171 non-null float64 analysis_url 1171 non-null object danceability 1171 non-null float64 duration_ms 1171 non-null int64 energy 1171 non-null float64 id 1171 non-null object instrumentalness 1171 non-null float64 key 1171 non-null int64 liveness 1171 non-null float64 loudness 1171 non-null float64 mode 1171 non-null int64 speechiness 1171 non-null float64 tempo 1171 non-null float64 time_signature 1171 non-null int64 track_href 1171 non-null object type 1171 non-null object uri 1171 non-null object valence 1171 non-null float64 dtypes: float64(9), int64(4), object(5) memory usage: 164.8+ KB
#SONG METRICS DATA CLEANUP
columns_to_drop_met = ['analysis_url','track_href','type','uri']
dfmet.drop(columns_to_drop_met, axis=1,inplace=True)
dfmet.rename(columns={'id': 'track_id'}, inplace=True)
dfmet.shape
dfmet.info()
dfmet.head()
#Song metrics collection is done here
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1171 entries, 0 to 1170 Data columns (total 14 columns): acousticness 1171 non-null float64 danceability 1171 non-null float64 duration_ms 1171 non-null int64 energy 1171 non-null float64 track_id 1171 non-null object instrumentalness 1171 non-null float64 key 1171 non-null int64 liveness 1171 non-null float64 loudness 1171 non-null float64 mode 1171 non-null int64 speechiness 1171 non-null float64 tempo 1171 non-null float64 time_signature 1171 non-null int64 valence 1171 non-null float64 dtypes: float64(9), int64(4), object(1) memory usage: 128.2+ KB
| acousticness | danceability | duration_ms | energy | track_id | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0180 | 0.584 | 228173 | 0.752 | 0zCfmceT1YvtZXGbR6Roht | 0.000004 | 8 | 0.647 | -6.689 | 1 | 0.0580 | 125.797 | 4 | 0.791 |
| 1 | 0.0834 | 0.543 | 192093 | 0.848 | 3gGHAScxUDsJqcAM6ZvjuH | 0.000000 | 10 | 0.799 | -6.058 | 0 | 0.0531 | 102.009 | 4 | 0.290 |
| 2 | 0.0340 | 0.818 | 225983 | 0.803 | 6b8Be6ljOzmkOmFslEb23P | 0.000000 | 1 | 0.153 | -4.282 | 1 | 0.0797 | 106.970 | 4 | 0.632 |
| 3 | 0.0736 | 0.608 | 185352 | 0.798 | 6DNtNfH8hXkqOX1sjqmI7p | 0.000000 | 6 | 0.156 | -5.092 | 0 | 0.0432 | 92.943 | 4 | 0.501 |
| 4 | 0.1010 | 0.707 | 254739 | 0.594 | 1dzk5fQIYJ3MMmgoRVw2qw | 0.002140 | 5 | 0.122 | -7.461 | 1 | 0.0470 | 88.000 | 4 | 0.193 |
#GET SONG NAME FROM PLAYLIST
# create empty lists where the results are going to be stored
artist_name = []
track_name = []
popularity = []
track_id = []
explicit = []
#Get playlist
track_results = get_playlist_tracks(userid, playlistURI)
# track_results = sp.user_playlist(userid, playlistURI)["tracks"]["items"] #edit this to put in your spotify playlist URI code
track_results
[{'added_at': '2017-12-05T13:44:23Z',
'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
'href': 'https://api.spotify.com/v1/users/gjordj',
'id': 'gjordj',
'type': 'user',
'uri': 'spotify:user:gjordj'},
'is_local': False,
'primary_color': None,
'track': {'album': {'album_type': 'compilation',
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0LyfQWJT6nXafLPZqxe9Of'},
'href': 'https://api.spotify.com/v1/artists/0LyfQWJT6nXafLPZqxe9Of',
'id': '0LyfQWJT6nXafLPZqxe9Of',
'name': 'Various Artists',
'type': 'artist',
'uri': 'spotify:artist:0LyfQWJT6nXafLPZqxe9Of'}],
'available_markets': [],
'external_urls': {'spotify': 'https://open.spotify.com/album/5p0H50uFCdWTpLY640HoPc'},
'href': 'https://api.spotify.com/v1/albums/5p0H50uFCdWTpLY640HoPc',
'id': '5p0H50uFCdWTpLY640HoPc',
'images': [{'height': 640,
'url': 'https://i.scdn.co/image/ab67616d0000b2732a307d64201d08144f68cc49',
'width': 640},
{'height': 300,
'url': 'https://i.scdn.co/image/ab67616d00001e022a307d64201d08144f68cc49',
'width': 300},
{'height': 64,
'url': 'https://i.scdn.co/image/ab67616d000048512a307d64201d08144f68cc49',
'width': 64}],
'name': 'La La Land (Original Motion Picture Soundtrack)',
'release_date': '2016-12-09',
'release_date_precision': 'day',
'total_tracks': 15,
'type': 'album',
'uri': 'spotify:album:5p0H50uFCdWTpLY640HoPc'},
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/6T70tWxqyIpdVbsFvPzqM3'},
'href': 'https://api.spotify.com/v1/artists/6T70tWxqyIpdVbsFvPzqM3',
'id': '6T70tWxqyIpdVbsFvPzqM3',
'name': 'La La Land Cast',
'type': 'artist',
'uri': 'spotify:artist:6T70tWxqyIpdVbsFvPzqM3'}],
'available_markets': [],
'disc_number': 1,
'duration_ms': 228173,
'episode': False,
'explicit': False,
'external_ids': {'isrc': 'USUG11600653'},
'external_urls': {'spotify': 'https://open.spotify.com/track/0zCfmceT1YvtZXGbR6Roht'},
'href': 'https://api.spotify.com/v1/tracks/0zCfmceT1YvtZXGbR6Roht',
'id': '0zCfmceT1YvtZXGbR6Roht',
'is_local': False,
'name': 'Another Day Of Sun - From "La La Land" Soundtrack',
'popularity': 8,
'preview_url': None,
'track': True,
'track_number': 1,
'type': 'track',
'uri': 'spotify:track:0zCfmceT1YvtZXGbR6Roht'},
'video_thumbnail': {'url': None}},
{'added_at': '2017-12-05T13:44:23Z',
'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
'href': 'https://api.spotify.com/v1/users/gjordj',
'id': 'gjordj',
'type': 'user',
'uri': 'spotify:user:gjordj'},
'is_local': False,
'primary_color': None,
'track': {'album': {'album_type': 'compilation',
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0LyfQWJT6nXafLPZqxe9Of'},
'href': 'https://api.spotify.com/v1/artists/0LyfQWJT6nXafLPZqxe9Of',
'id': '0LyfQWJT6nXafLPZqxe9Of',
'name': 'Various Artists',
'type': 'artist',
'uri': 'spotify:artist:0LyfQWJT6nXafLPZqxe9Of'}],
'available_markets': [],
'external_urls': {'spotify': 'https://open.spotify.com/album/5p0H50uFCdWTpLY640HoPc'},
'href': 'https://api.spotify.com/v1/albums/5p0H50uFCdWTpLY640HoPc',
'id': '5p0H50uFCdWTpLY640HoPc',
'images': [{'height': 640,
'url': 'https://i.scdn.co/image/ab67616d0000b2732a307d64201d08144f68cc49',
'width': 640},
{'height': 300,
'url': 'https://i.scdn.co/image/ab67616d00001e022a307d64201d08144f68cc49',
'width': 300},
{'height': 64,
'url': 'https://i.scdn.co/image/ab67616d000048512a307d64201d08144f68cc49',
'width': 64}],
'name': 'La La Land (Original Motion Picture Soundtrack)',
'release_date': '2016-12-09',
'release_date_precision': 'day',
'total_tracks': 15,
'type': 'album',
'uri': 'spotify:album:5p0H50uFCdWTpLY640HoPc'},
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/5y2Xq6xcjJb2jVM54GHK3t'},
'href': 'https://api.spotify.com/v1/artists/5y2Xq6xcjJb2jVM54GHK3t',
'id': '5y2Xq6xcjJb2jVM54GHK3t',
'name': 'John Legend',
'type': 'artist',
'uri': 'spotify:artist:5y2Xq6xcjJb2jVM54GHK3t'}],
'available_markets': [],
'disc_number': 1,
'duration_ms': 192093,
'episode': False,
'explicit': False,
'external_ids': {'isrc': 'USUG11600664'},
'external_urls': {'spotify': 'https://open.spotify.com/track/3gGHAScxUDsJqcAM6ZvjuH'},
'href': 'https://api.spotify.com/v1/tracks/3gGHAScxUDsJqcAM6ZvjuH',
'id': '3gGHAScxUDsJqcAM6ZvjuH',
'is_local': False,
'name': 'Start A Fire',
'popularity': 5,
'preview_url': None,
'track': True,
'track_number': 10,
'type': 'track',
'uri': 'spotify:track:3gGHAScxUDsJqcAM6ZvjuH'},
'video_thumbnail': {'url': None}},
{'added_at': '2017-12-05T13:44:23Z',
'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
'href': 'https://api.spotify.com/v1/users/gjordj',
'id': 'gjordj',
'type': 'user',
'uri': 'spotify:user:gjordj'},
'is_local': False,
'primary_color': None,
'track': {'album': {'album_type': 'album',
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0du5cEVh5yTK9QJze8zA0C'},
'href': 'https://api.spotify.com/v1/artists/0du5cEVh5yTK9QJze8zA0C',
'id': '0du5cEVh5yTK9QJze8zA0C',
'name': 'Bruno Mars',
'type': 'artist',
'uri': 'spotify:artist:0du5cEVh5yTK9QJze8zA0C'}],
'available_markets': ['AD',
'AE',
'AR',
'AT',
'AU',
'BE',
'BG',
'BH',
'BO',
'BR',
'CA',
'CH',
'CL',
'CO',
'CR',
'CY',
'CZ',
'DE',
'DK',
'DO',
'DZ',
'EC',
'EE',
'EG',
'ES',
'FI',
'FR',
'GB',
'GR',
'GT',
'HK',
'HN',
'HU',
'ID',
'IE',
'IL',
'IS',
'IT',
'JO',
'JP',
'KW',
'LB',
'LI',
'LT',
'LU',
'LV',
'MA',
'MC',
'MT',
'MX',
'MY',
'NI',
'NL',
'NO',
'NZ',
'OM',
'PA',
'PE',
'PH',
'PL',
'PS',
'PT',
'PY',
'QA',
'RO',
'SA',
'SE',
'SG',
'SK',
'SV',
'TH',
'TN',
'TR',
'TW',
'US',
'UY',
'VN',
'ZA'],
'external_urls': {'spotify': 'https://open.spotify.com/album/4PgleR09JVnm3zY1fW3XBA'},
'href': 'https://api.spotify.com/v1/albums/4PgleR09JVnm3zY1fW3XBA',
'id': '4PgleR09JVnm3zY1fW3XBA',
'images': [{'height': 640,
'url': 'https://i.scdn.co/image/ab67616d0000b273232711f7d66a1e19e89e28c5',
'width': 640},
{'height': 300,
'url': 'https://i.scdn.co/image/ab67616d00001e02232711f7d66a1e19e89e28c5',
'width': 300},
{'height': 64,
'url': 'https://i.scdn.co/image/ab67616d00004851232711f7d66a1e19e89e28c5',
'width': 64}],
'name': '24K Magic',
'release_date': '2016-11-17',
'release_date_precision': 'day',
'total_tracks': 9,
'type': 'album',
'uri': 'spotify:album:4PgleR09JVnm3zY1fW3XBA'},
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0du5cEVh5yTK9QJze8zA0C'},
'href': 'https://api.spotify.com/v1/artists/0du5cEVh5yTK9QJze8zA0C',
'id': '0du5cEVh5yTK9QJze8zA0C',
'name': 'Bruno Mars',
'type': 'artist',
'uri': 'spotify:artist:0du5cEVh5yTK9QJze8zA0C'}],
'available_markets': ['AD',
'AE',
'AR',
'AT',
'AU',
'BE',
'BG',
'BH',
'BO',
'BR',
'CA',
'CH',
'CL',
'CO',
'CR',
'CY',
'CZ',
'DE',
'DK',
'DO',
'DZ',
'EC',
'EE',
'EG',
'ES',
'FI',
'FR',
'GB',
'GR',
'GT',
'HK',
'HN',
'HU',
'ID',
'IE',
'IL',
'IS',
'IT',
'JO',
'JP',
'KW',
'LB',
'LI',
'LT',
'LU',
'LV',
'MA',
'MC',
'MT',
'MX',
'MY',
'NI',
'NL',
'NO',
'NZ',
'OM',
'PA',
'PE',
'PH',
'PL',
'PS',
'PT',
'PY',
'QA',
'RO',
'SA',
'SE',
'SG',
'SK',
'SV',
'TH',
'TN',
'TR',
'TW',
'US',
'UY',
'VN',
'ZA'],
'disc_number': 1,
'duration_ms': 225983,
'episode': False,
'explicit': False,
'external_ids': {'isrc': 'USAT21602944'},
'external_urls': {'spotify': 'https://open.spotify.com/track/6b8Be6ljOzmkOmFslEb23P'},
'href': 'https://api.spotify.com/v1/tracks/6b8Be6ljOzmkOmFslEb23P',
'id': '6b8Be6ljOzmkOmFslEb23P',
'is_local': False,
'name': '24K Magic',
'popularity': 77,
'preview_url': 'https://p.scdn.co/mp3-preview/3213a35ec32d4c747e794072a5ef7b464b4fcb69?cid=b43c7befbf6a4de29308d2dd70056f20',
'track': True,
'track_number': 1,
'type': 'track',
'uri': 'spotify:track:6b8Be6ljOzmkOmFslEb23P'},
'video_thumbnail': {'url': None}},
{'added_at': '2017-12-05T13:44:23Z',
'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
'href': 'https://api.spotify.com/v1/users/gjordj',
'id': 'gjordj',
'type': 'user',
'uri': 'spotify:user:gjordj'},
'is_local': False,
'primary_color': None,
'track': {'album': {'album_type': 'single',
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/738wLrAtLtCtFOLvQBXOXp'},
'href': 'https://api.spotify.com/v1/artists/738wLrAtLtCtFOLvQBXOXp',
'id': '738wLrAtLtCtFOLvQBXOXp',
'name': 'Major Lazer',
'type': 'artist',
'uri': 'spotify:artist:738wLrAtLtCtFOLvQBXOXp'}],
'available_markets': [],
'external_urls': {'spotify': 'https://open.spotify.com/album/3o99825qhG7K7D07naRs4F'},
'href': 'https://api.spotify.com/v1/albums/3o99825qhG7K7D07naRs4F',
'id': '3o99825qhG7K7D07naRs4F',
'images': [{'height': 640,
'url': 'https://i.scdn.co/image/ab67616d0000b2732e8f1cca2bc8c9d55930523b',
'width': 640},
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'AE',
'AR',
'AT',
'AU',
'BE',
'BG',
'BH',
'BO',
'BR',
'CA',
'CH',
'CL',
'CO',
'CR',
'CY',
'CZ',
'DE',
'DK',
'DO',
'DZ',
'EC',
'EE',
'EG',
'ES',
'FI',
'FR',
'GB',
'GR',
'GT',
'HK',
'HN',
'HU',
'ID',
'IE',
'IL',
'IS',
'IT',
'JO',
'JP',
'KW',
'LB',
'LI',
'LT',
'LU',
'LV',
'MA',
'MC',
'MT',
'MX',
'MY',
'NI',
'NL',
'NO',
'NZ',
'OM',
'PA',
'PE',
'PH',
'PL',
'PS',
'PT',
'PY',
'QA',
'RO',
'SA',
'SE',
'SG',
'SK',
'SV',
'TH',
'TN',
'TR',
'TW',
'US',
'UY',
'VN',
'ZA'],
'external_urls': {'spotify': 'https://open.spotify.com/album/2PTte9WD2NqJzmbqNaDpa1'},
'href': 'https://api.spotify.com/v1/albums/2PTte9WD2NqJzmbqNaDpa1',
'id': '2PTte9WD2NqJzmbqNaDpa1',
'images': [{'height': 640,
'url': 'https://i.scdn.co/image/ac5d40b67ecfbcec28c9e70fce15aff04b06cb9f',
'width': 640},
{'height': 300,
'url': 'https://i.scdn.co/image/ea83c4a7c7befce125bd9e3e7b308a7eea7482e8',
'width': 300},
{'height': 64,
'url': 'https://i.scdn.co/image/845f656e26f657cd3fbc43b8ce032feac1387aa0',
'width': 64}],
'name': 'Good Vibes - The Very Best of Laid Back',
'release_date': '2008-10-06',
'release_date_precision': 'day',
'total_tracks': 32,
'type': 'album',
'uri': 'spotify:album:2PTte9WD2NqJzmbqNaDpa1'},
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/2OLmN3LLWtLF7zerL4VdaX'},
'href': 'https://api.spotify.com/v1/artists/2OLmN3LLWtLF7zerL4VdaX',
'id': '2OLmN3LLWtLF7zerL4VdaX',
'name': 'Laid Back',
'type': 'artist',
'uri': 'spotify:artist:2OLmN3LLWtLF7zerL4VdaX'}],
'available_markets': ['AD',
'AE',
'AR',
'AT',
'AU',
'BE',
'BG',
'BH',
'BO',
'BR',
'CA',
'CH',
'CL',
'CO',
'CR',
'CY',
'CZ',
'DE',
'DK',
'DO',
'DZ',
'EC',
'EE',
'EG',
'ES',
'FI',
'FR',
'GB',
'GR',
'GT',
'HK',
'HN',
'HU',
'ID',
'IE',
'IL',
'IS',
'IT',
'JO',
'JP',
'KW',
'LB',
'LI',
'LT',
'LU',
'LV',
'MA',
'MC',
'MT',
'MX',
'MY',
'NI',
'NL',
'NO',
'NZ',
'OM',
'PA',
'PE',
'PH',
'PL',
'PS',
'PT',
'PY',
'QA',
'RO',
'SA',
'SE',
'SG',
'SK',
'SV',
'TH',
'TN',
'TR',
'TW',
'US',
'UY',
'VN',
'ZA'],
'disc_number': 1,
'duration_ms': 237320,
'episode': False,
'explicit': False,
'external_ids': {'isrc': 'DKABA9760003'},
'external_urls': {'spotify': 'https://open.spotify.com/track/3FeQweSLSbVkLcPDZuTiGX'},
'href': 'https://api.spotify.com/v1/tracks/3FeQweSLSbVkLcPDZuTiGX',
'id': '3FeQweSLSbVkLcPDZuTiGX',
'is_local': False,
'name': 'White Horse',
'popularity': 43,
'preview_url': 'https://p.scdn.co/mp3-preview/89ea1e6c29056a0322eef40b247e6300ad04e304?cid=b43c7befbf6a4de29308d2dd70056f20',
'track': True,
'track_number': 4,
'type': 'track',
'uri': 'spotify:track:3FeQweSLSbVkLcPDZuTiGX'},
'video_thumbnail': {'url': None}},
{'added_at': '2018-05-18T19:10:19Z',
'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
'href': 'https://api.spotify.com/v1/users/gjordj',
'id': 'gjordj',
'type': 'user',
'uri': 'spotify:user:gjordj'},
'is_local': False,
'primary_color': None,
'track': {'album': {'album_type': 'album',
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/3gMaNLQm7D9MornNILzdSl'},
'href': 'https://api.spotify.com/v1/artists/3gMaNLQm7D9MornNILzdSl',
'id': '3gMaNLQm7D9MornNILzdSl',
'name': 'Lionel Richie',
'type': 'artist',
'uri': 'spotify:artist:3gMaNLQm7D9MornNILzdSl'}],
'available_markets': ['CA', 'MX', 'US'],
'external_urls': {'spotify': 'https://open.spotify.com/album/3alZBOvPaK3hgMEEymw4Yr'},
'href': 'https://api.spotify.com/v1/albums/3alZBOvPaK3hgMEEymw4Yr',
'id': '3alZBOvPaK3hgMEEymw4Yr',
'images': [{'height': 637,
'url': 'https://i.scdn.co/image/a517f00f361e5429d15f0b2a6f6c06be73782c19',
'width': 640},
{'height': 299,
'url': 'https://i.scdn.co/image/ebce2fbd07a3d5ae7a8fbb962051a3123f93a1d4',
'width': 300},
{'height': 64,
'url': 'https://i.scdn.co/image/957ce5e026d1c27e71fcc4f32f5cb305d30282f7',
'width': 64}],
'name': "Can't Slow Down",
'release_date': '1983-01-01',
'release_date_precision': 'day',
'total_tracks': 8,
'type': 'album',
'uri': 'spotify:album:3alZBOvPaK3hgMEEymw4Yr'},
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/3gMaNLQm7D9MornNILzdSl'},
'href': 'https://api.spotify.com/v1/artists/3gMaNLQm7D9MornNILzdSl',
'id': '3gMaNLQm7D9MornNILzdSl',
'name': 'Lionel Richie',
'type': 'artist',
'uri': 'spotify:artist:3gMaNLQm7D9MornNILzdSl'}],
'available_markets': ['CA', 'MX', 'US'],
'disc_number': 1,
'duration_ms': 195133,
'episode': False,
'explicit': False,
'external_ids': {'isrc': 'USMO18390015'},
'external_urls': {'spotify': 'https://open.spotify.com/track/0ROwoz82DIW4tOzkxYnSjf'},
'href': 'https://api.spotify.com/v1/tracks/0ROwoz82DIW4tOzkxYnSjf',
'id': '0ROwoz82DIW4tOzkxYnSjf',
'is_local': False,
'name': 'Stuck On You',
'popularity': 59,
'preview_url': None,
'track': True,
'track_number': 4,
'type': 'track',
'uri': 'spotify:track:0ROwoz82DIW4tOzkxYnSjf'},
'video_thumbnail': {'url': None}},
{'added_at': '2018-05-18T19:10:19Z',
'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
'href': 'https://api.spotify.com/v1/users/gjordj',
'id': 'gjordj',
'type': 'user',
'uri': 'spotify:user:gjordj'},
'is_local': False,
'primary_color': None,
'track': {'album': {'album_type': 'compilation',
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/0LyfQWJT6nXafLPZqxe9Of'},
'href': 'https://api.spotify.com/v1/artists/0LyfQWJT6nXafLPZqxe9Of',
'id': '0LyfQWJT6nXafLPZqxe9Of',
'name': 'Various Artists',
'type': 'artist',
'uri': 'spotify:artist:0LyfQWJT6nXafLPZqxe9Of'}],
'available_markets': [],
'external_urls': {'spotify': 'https://open.spotify.com/album/11N1EkkhbkBS2awMJ7mY4W'},
'href': 'https://api.spotify.com/v1/albums/11N1EkkhbkBS2awMJ7mY4W',
'id': '11N1EkkhbkBS2awMJ7mY4W',
'images': [{'height': 640,
'url': 'https://i.scdn.co/image/ab67616d0000b27345d84ef207014acef6e4b482',
'width': 640},
{'height': 300,
'url': 'https://i.scdn.co/image/ab67616d00001e0245d84ef207014acef6e4b482',
'width': 300},
{'height': 64,
'url': 'https://i.scdn.co/image/ab67616d0000485145d84ef207014acef6e4b482',
'width': 64}],
'name': 'Son Of Rambow (Music From The Motion Picture)',
'release_date': '2007',
'release_date_precision': 'year',
'total_tracks': 21,
'type': 'album',
'uri': 'spotify:album:11N1EkkhbkBS2awMJ7mY4W'},
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/2The4Ur661sLPGndcUuuLu'},
'href': 'https://api.spotify.com/v1/artists/2The4Ur661sLPGndcUuuLu',
'id': '2The4Ur661sLPGndcUuuLu',
'name': 'Nu Shooz',
'type': 'artist',
'uri': 'spotify:artist:2The4Ur661sLPGndcUuuLu'}],
'available_markets': [],
'disc_number': 1,
'duration_ms': 220386,
'episode': False,
'explicit': False,
'external_ids': {'isrc': 'USGMP0800022'},
'external_urls': {'spotify': 'https://open.spotify.com/track/044fQJmT2fZUYcA3BnaOL2'},
'href': 'https://api.spotify.com/v1/tracks/044fQJmT2fZUYcA3BnaOL2',
'id': '044fQJmT2fZUYcA3BnaOL2',
'is_local': False,
'name': "I Can't Wait",
'popularity': 2,
'preview_url': None,
'track': True,
'track_number': 3,
'type': 'track',
'uri': 'spotify:track:044fQJmT2fZUYcA3BnaOL2'},
'video_thumbnail': {'url': None}},
{'added_at': '2018-05-18T19:10:19Z',
'added_by': {'external_urls': {'spotify': 'https://open.spotify.com/user/gjordj'},
'href': 'https://api.spotify.com/v1/users/gjordj',
'id': 'gjordj',
'type': 'user',
'uri': 'spotify:user:gjordj'},
'is_local': False,
'primary_color': None,
'track': {'album': {'album_type': 'album',
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/3q8tRS0hCMVCylgKAoA0ya'},
'href': 'https://api.spotify.com/v1/artists/3q8tRS0hCMVCylgKAoA0ya',
'id': '3q8tRS0hCMVCylgKAoA0ya',
'name': 'Stacey Q',
'type': 'artist',
'uri': 'spotify:artist:3q8tRS0hCMVCylgKAoA0ya'}],
'available_markets': ['AD',
'AE',
'AR',
'AT',
'AU',
'BE',
'BG',
'BH',
'BO',
'BR',
'CA',
'CH',
'CL',
'CO',
'CR',
'CY',
'CZ',
'DE',
'DK',
'DO',
'DZ',
'EC',
'EE',
'EG',
'ES',
'FI',
'FR',
'GB',
'GR',
'GT',
'HK',
'HN',
'HU',
'ID',
'IE',
'IL',
'IS',
'IT',
'JO',
'JP',
'KW',
'LB',
'LI',
'LT',
'LU',
'LV',
'MA',
'MC',
'MT',
'MX',
'MY',
'NI',
'NL',
'NO',
'NZ',
'OM',
'PA',
'PE',
'PH',
'PL',
'PS',
'PT',
'PY',
'QA',
'RO',
'SA',
'SE',
'SG',
'SK',
'SV',
'TH',
'TN',
'TR',
'TW',
'US',
'UY',
'VN',
'ZA'],
'external_urls': {'spotify': 'https://open.spotify.com/album/71st36nPTXAgWnEaNg9AZa'},
'href': 'https://api.spotify.com/v1/albums/71st36nPTXAgWnEaNg9AZa',
'id': '71st36nPTXAgWnEaNg9AZa',
'images': [{'height': 640,
'url': 'https://i.scdn.co/image/ab67616d0000b2734fbef9faaac04982d6170c85',
'width': 640},
{'height': 300,
'url': 'https://i.scdn.co/image/ab67616d00001e024fbef9faaac04982d6170c85',
'width': 300},
{'height': 64,
'url': 'https://i.scdn.co/image/ab67616d000048514fbef9faaac04982d6170c85',
'width': 64}],
'name': 'Better Than Heaven',
'release_date': '1986-08-18',
'release_date_precision': 'day',
'total_tracks': 10,
'type': 'album',
'uri': 'spotify:album:71st36nPTXAgWnEaNg9AZa'},
'artists': [{'external_urls': {'spotify': 'https://open.spotify.com/artist/3q8tRS0hCMVCylgKAoA0ya'},
'href': 'https://api.spotify.com/v1/artists/3q8tRS0hCMVCylgKAoA0ya',
'id': '3q8tRS0hCMVCylgKAoA0ya',
'name': 'Stacey Q',
'type': 'artist',
'uri': 'spotify:artist:3q8tRS0hCMVCylgKAoA0ya'}],
'available_markets': ['AD',
'AE',
'AR',
'AT',
'AU',
'BE',
'BG',
'BH',
'BO',
'BR',
'CA',
'CH',
'CL',
'CO',
'CR',
'CY',
'CZ',
'DE',
'DK',
'DO',
'DZ',
'EC',
'EE',
'EG',
'ES',
'FI',
'FR',
'GB',
'GR',
'GT',
'HK',
'HN',
'HU',
'ID',
'IE',
'IL',
'IS',
'IT',
'JO',
'JP',
'KW',
'LB',
'LI',
'LT',
'LU',
'LV',
'MA',
'MC',
'MT',
'MX',
'MY',
'NI',
'NL',
'NO',
'NZ',
'OM',
'PA',
'PE',
'PH',
'PL',
'PS',
'PT',
'PY',
'QA',
'RO',
'SA',
'SE',
'SG',
'SK',
'SV',
'TH',
'TN',
'TR',
'TW',
'US',
'UY',
'VN',
'ZA'],
'disc_number': 1,
'duration_ms': 237533,
'episode': False,
'explicit': False,
'external_ids': {'isrc': 'USAT29902109'},
'external_urls': {'spotify': 'https://open.spotify.com/track/2dURQIBrw3XcHyVZlfdpC1'},
'href': 'https://api.spotify.com/v1/tracks/2dURQIBrw3XcHyVZlfdpC1',
'id': '2dURQIBrw3XcHyVZlfdpC1',
'is_local': False,
'name': 'Two of Hearts',
'popularity': 53,
'preview_url': 'https://p.scdn.co/mp3-preview/9426c57cd0292f6d5470520cee905828c8071a3c?cid=b43c7befbf6a4de29308d2dd70056f20',
'track': True,
'track_number': 1,
'type': 'track',
'uri': 'spotify:track:2dURQIBrw3XcHyVZlfdpC1'},
'video_thumbnail': {'url': None}},
...]
for t in track_results:
artist_name.append(t['track']['artists'][0]['name'])
track_name.append(t['track']['name'])
track_id.append(t['track']['id'])
popularity.append(t['track']['popularity'])
explicit.append(t['track']['explicit'])
print('number of elements in the track_id list:', len(track_id))
number of elements in the track_id list: 2342
dftracks = pd.DataFrame({'artist_name':artist_name,
'track_name':track_name,
'track_id':track_id,
'popularity':popularity,
'explicit':explicit})
#print(df_tracks.shape)
dftracks.shape
dftracks.info()
dftracks.head()#print(df_tracks.shape)
dftracks.shape
dftracks.info()
dftracks.head()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2342 entries, 0 to 2341 Data columns (total 5 columns): artist_name 2342 non-null object track_name 2342 non-null object track_id 2342 non-null object popularity 2342 non-null int64 explicit 2342 non-null bool dtypes: bool(1), int64(1), object(3) memory usage: 75.6+ KB <class 'pandas.core.frame.DataFrame'> RangeIndex: 2342 entries, 0 to 2341 Data columns (total 5 columns): artist_name 2342 non-null object track_name 2342 non-null object track_id 2342 non-null object popularity 2342 non-null int64 explicit 2342 non-null bool dtypes: bool(1), int64(1), object(3) memory usage: 75.6+ KB
| artist_name | track_name | track_id | popularity | explicit | |
|---|---|---|---|---|---|
| 0 | La La Land Cast | Another Day Of Sun - From "La La Land" Soundtrack | 0zCfmceT1YvtZXGbR6Roht | 8 | False |
| 1 | John Legend | Start A Fire | 3gGHAScxUDsJqcAM6ZvjuH | 5 | False |
| 2 | Bruno Mars | 24K Magic | 6b8Be6ljOzmkOmFslEb23P | 77 | False |
| 3 | Major Lazer | Cold Water (feat. Justin Bieber & MØ) | 6DNtNfH8hXkqOX1sjqmI7p | 19 | False |
| 4 | Fort Frances | Summertime | 1dzk5fQIYJ3MMmgoRVw2qw | 41 | False |
#SONG NAME DATA CLEANUP
grouped = dftracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped[grouped > 1].count()
dftracks.drop_duplicates(subset=['artist_name','track_name'], inplace=True)
# doing the same grouping as before to verify the solution
grouped_after_dropping = dftracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped_after_dropping[grouped_after_dropping > 1].count()
dftracks[dftracks.duplicated(subset=['artist_name','track_name'],keep=False)].count()
dftracks.shape
# MERGE BOTH DATAFRAMES
# the 'inner' method will make sure to keep track IDs present in both datasets
dftotal = pd.merge(dftracks,dfmet,on='track_id',how='inner')
dftotal.head()
dftotal.info()
dftotal[dftotal.duplicated(subset=['artist_name','track_name'],keep=False)]
<class 'pandas.core.frame.DataFrame'> Int64Index: 1162 entries, 0 to 1161 Data columns (total 18 columns): artist_name 1162 non-null object track_name 1162 non-null object track_id 1162 non-null object popularity 1162 non-null int64 explicit 1162 non-null bool acousticness 1162 non-null float64 danceability 1162 non-null float64 duration_ms 1162 non-null int64 energy 1162 non-null float64 instrumentalness 1162 non-null float64 key 1162 non-null int64 liveness 1162 non-null float64 loudness 1162 non-null float64 mode 1162 non-null int64 speechiness 1162 non-null float64 tempo 1162 non-null float64 time_signature 1162 non-null int64 valence 1162 non-null float64 dtypes: bool(1), float64(9), int64(5), object(3) memory usage: 164.5+ KB
| artist_name | track_name | track_id | popularity | explicit | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 663 | 2Pac | Do For Love | 3dSGqXMll4UJUohLANG0ce | 6 | True | 0.05160 | 0.861 | 281600 | 0.638 | 0.002240 | 10 | 0.0650 | -3.878 | 0 | 0.3230 | 95.012 | 4 | 0.765 |
| 664 | 2Pac | Do For Love | 3dSGqXMll4UJUohLANG0ce | 6 | True | 0.05160 | 0.861 | 281600 | 0.638 | 0.002240 | 10 | 0.0650 | -3.878 | 0 | 0.3230 | 95.012 | 4 | 0.765 |
| 897 | Enrique Iglesias | Loco | 0Hv8aeWmZt5vPngIql4UDF | 1 | False | 0.37900 | 0.754 | 213667 | 0.860 | 0.000000 | 7 | 0.1110 | -3.368 | 1 | 0.0285 | 132.964 | 4 | 0.874 |
| 898 | Enrique Iglesias | Loco | 0Hv8aeWmZt5vPngIql4UDF | 1 | False | 0.37900 | 0.754 | 213667 | 0.860 | 0.000000 | 7 | 0.1110 | -3.368 | 1 | 0.0285 | 132.964 | 4 | 0.874 |
| 906 | Pharrell Williams | Happy - From "Despicable Me 2" | 3RkvscbM4aNbELiQf7PJwn | 13 | False | 0.29100 | 0.662 | 233293 | 0.757 | 0.000000 | 5 | 0.0885 | -6.818 | 0 | 0.1440 | 159.945 | 4 | 0.962 |
| 907 | Pharrell Williams | Happy - From "Despicable Me 2" | 3RkvscbM4aNbELiQf7PJwn | 13 | False | 0.29100 | 0.662 | 233293 | 0.757 | 0.000000 | 5 | 0.0885 | -6.818 | 0 | 0.1440 | 159.945 | 4 | 0.962 |
| 911 | Faul & Wad Ad | Changes | 13qqdlSeF8FcxsRyapDMZ0 | 35 | False | 0.00864 | 0.822 | 345173 | 0.704 | 0.627000 | 3 | 0.0633 | -8.242 | 0 | 0.0374 | 125.999 | 4 | 0.255 |
| 912 | Faul & Wad Ad | Changes | 13qqdlSeF8FcxsRyapDMZ0 | 35 | False | 0.00864 | 0.822 | 345173 | 0.704 | 0.627000 | 3 | 0.0633 | -8.242 | 0 | 0.0374 | 125.999 | 4 | 0.255 |
| 946 | Elmore James | Dust My Broom | 3LGB6BkiSmzP8IHo03rZMo | 39 | False | 0.24700 | 0.526 | 174667 | 0.664 | 0.012400 | 2 | 0.2890 | -6.620 | 1 | 0.0354 | 101.928 | 4 | 0.797 |
| 947 | Elmore James | Dust My Broom | 3LGB6BkiSmzP8IHo03rZMo | 39 | False | 0.24700 | 0.526 | 174667 | 0.664 | 0.012400 | 2 | 0.2890 | -6.620 | 1 | 0.0354 | 101.928 | 4 | 0.797 |
| 950 | Cannonball Adderley | Mercy, Mercy, Mercy | 7aBKUvEYylCA24Cn8vepJj | 28 | False | 0.70200 | 0.398 | 308507 | 0.275 | 0.151000 | 10 | 0.9780 | -16.175 | 1 | 0.0386 | 170.076 | 4 | 0.436 |
| 951 | Cannonball Adderley | Mercy, Mercy, Mercy | 7aBKUvEYylCA24Cn8vepJj | 28 | False | 0.70200 | 0.398 | 308507 | 0.275 | 0.151000 | 10 | 0.9780 | -16.175 | 1 | 0.0386 | 170.076 | 4 | 0.436 |
| 952 | Ian Dury | Hit Me With Your Rhythm Stick | 4txFRJmpBJCRfDIAc30yTB | 26 | True | 0.07390 | 0.713 | 223133 | 0.861 | 0.001730 | 5 | 0.1140 | -7.834 | 0 | 0.0360 | 104.299 | 4 | 0.898 |
| 953 | Ian Dury | Hit Me With Your Rhythm Stick | 4txFRJmpBJCRfDIAc30yTB | 26 | True | 0.07390 | 0.713 | 223133 | 0.861 | 0.001730 | 5 | 0.1140 | -7.834 | 0 | 0.0360 | 104.299 | 4 | 0.898 |
| 954 | Clyde McCoy | Tear It Down | 3FZ43CoJqo5iU4XQ2zwZCD | 12 | False | 0.98200 | 0.875 | 183493 | 0.425 | 0.116000 | 5 | 0.1080 | -11.124 | 1 | 0.0915 | 114.792 | 4 | 0.874 |
| 955 | Clyde McCoy | Tear It Down | 3FZ43CoJqo5iU4XQ2zwZCD | 12 | False | 0.98200 | 0.875 | 183493 | 0.425 | 0.116000 | 5 | 0.1080 | -11.124 | 1 | 0.0915 | 114.792 | 4 | 0.874 |
| 957 | Plastic Bertrand | Ca plane pour moi | 71yCMlsD6qbD7NmNUEoVNR | 59 | False | 0.07400 | 0.422 | 182133 | 0.943 | 0.000068 | 10 | 0.1050 | -4.365 | 1 | 0.0954 | 165.764 | 4 | 0.845 |
| 958 | Plastic Bertrand | Ca plane pour moi | 71yCMlsD6qbD7NmNUEoVNR | 59 | False | 0.07400 | 0.422 | 182133 | 0.943 | 0.000068 | 10 | 0.1050 | -4.365 | 1 | 0.0954 | 165.764 | 4 | 0.845 |
| 960 | Charles Mingus | Wednesday Night Prayer Meeting | 74EiaLmtaOlfKZYFkhHs6J | 30 | False | 0.68700 | 0.364 | 342480 | 0.470 | 0.002540 | 5 | 0.0849 | -13.051 | 0 | 0.0730 | 86.900 | 4 | 0.486 |
| 961 | Charles Mingus | Wednesday Night Prayer Meeting | 74EiaLmtaOlfKZYFkhHs6J | 30 | False | 0.68700 | 0.364 | 342480 | 0.470 | 0.002540 | 5 | 0.0849 | -13.051 | 0 | 0.0730 | 86.900 | 4 | 0.486 |
| 1038 | Sandy Marton | People from Ibiza | 0OcAaaQfHvawA7F0n6Wha6 | 2 | False | 0.01820 | 0.722 | 306360 | 0.705 | 0.318000 | 7 | 0.0579 | -9.156 | 1 | 0.0684 | 119.525 | 4 | 0.454 |
| 1039 | Sandy Marton | People from Ibiza | 0OcAaaQfHvawA7F0n6Wha6 | 2 | False | 0.01820 | 0.722 | 306360 | 0.705 | 0.318000 | 7 | 0.0579 | -9.156 | 1 | 0.0684 | 119.525 | 4 | 0.454 |
| 1048 | José de Rico | Soltera (feat. Danny Romero & Fito Blanko) | 58xwJEkzxhyXzYyjELtOCN | 43 | False | 0.04210 | 0.629 | 172493 | 0.938 | 0.000000 | 0 | 0.2760 | -4.335 | 1 | 0.0471 | 128.031 | 4 | 0.949 |
| 1049 | José de Rico | Soltera (feat. Danny Romero & Fito Blanko) | 58xwJEkzxhyXzYyjELtOCN | 43 | False | 0.04210 | 0.629 | 172493 | 0.938 | 0.000000 | 0 | 0.2760 | -4.335 | 1 | 0.0471 | 128.031 | 4 | 0.949 |
| 1052 | The Whispers | And The Beat Goes On | 77OW25gumw4UBDvhXDVWdQ | 1 | False | 0.18100 | 0.804 | 205467 | 0.530 | 0.000339 | 11 | 0.0694 | -9.645 | 0 | 0.1390 | 115.226 | 4 | 0.873 |
| 1053 | The Whispers | And The Beat Goes On | 77OW25gumw4UBDvhXDVWdQ | 1 | False | 0.18100 | 0.804 | 205467 | 0.530 | 0.000339 | 11 | 0.0694 | -9.645 | 0 | 0.1390 | 115.226 | 4 | 0.873 |
| 1061 | The Angels | My Boyfriend's Back | 4jO6bQ15aDnMQplLL04cSy | 8 | False | 0.14700 | 0.759 | 127093 | 0.933 | 0.008020 | 3 | 0.1110 | -5.293 | 1 | 0.2170 | 135.458 | 4 | 0.801 |
| 1062 | The Angels | My Boyfriend's Back | 4jO6bQ15aDnMQplLL04cSy | 8 | False | 0.14700 | 0.759 | 127093 | 0.933 | 0.008020 | 3 | 0.1110 | -5.293 | 1 | 0.2170 | 135.458 | 4 | 0.801 |
#write to csv
dftotal.to_csv('../csv_files/datasetPlaylist.csv')
dfPlaylist = dftotal
dfPlaylist.head()
| artist_name | track_name | track_id | popularity | explicit | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | La La Land Cast | Another Day Of Sun - From "La La Land" Soundtrack | 0zCfmceT1YvtZXGbR6Roht | 8 | False | 0.0180 | 0.584 | 228173 | 0.752 | 0.000004 | 8 | 0.647 | -6.689 | 1 | 0.0580 | 125.797 | 4 | 0.791 |
| 1 | John Legend | Start A Fire | 3gGHAScxUDsJqcAM6ZvjuH | 5 | False | 0.0834 | 0.543 | 192093 | 0.848 | 0.000000 | 10 | 0.799 | -6.058 | 0 | 0.0531 | 102.009 | 4 | 0.290 |
| 2 | Bruno Mars | 24K Magic | 6b8Be6ljOzmkOmFslEb23P | 77 | False | 0.0340 | 0.818 | 225983 | 0.803 | 0.000000 | 1 | 0.153 | -4.282 | 1 | 0.0797 | 106.970 | 4 | 0.632 |
| 3 | Major Lazer | Cold Water (feat. Justin Bieber & MØ) | 6DNtNfH8hXkqOX1sjqmI7p | 19 | False | 0.0736 | 0.608 | 185352 | 0.798 | 0.000000 | 6 | 0.156 | -5.092 | 0 | 0.0432 | 92.943 | 4 | 0.501 |
| 4 | Fort Frances | Summertime | 1dzk5fQIYJ3MMmgoRVw2qw | 41 | False | 0.1010 | 0.707 | 254739 | 0.594 | 0.002140 | 5 | 0.122 | -7.461 | 1 | 0.0470 | 88.000 | 4 | 0.193 |
len(dfPlaylist)
1162
#GET SONG NAMES OF RANDOM SAMPLE
#####################################
sample_size = 5000 # number of songs per 'year'
#####################################
random.seed(435)
# create empty lists where the results are going to be stored
artist_name = []
track_name = []
popularity = []
track_id = []
explicit = []
year_song = []
seedIter = 0
yearNumber = ['year:2008', 'year:2009', 'year:2010', 'year:2011', 'year:2012',
'year:2013','year:2014','year:2015','year:2016',
'year:2017', 'year:2018', 'year:2019']
#random sample. set sample size, sample rate max 100, and song year
for j in yearNumber:
random.seed(seedIter)
for i in range(0,sample_size,10):
track_results = sp.search(q= j, type='track', limit=10,offset=i)
for i, t in enumerate(track_results['tracks']['items']):
artist_name.append(t['artists'][0]['name'])
track_name.append(t['name'])
track_id.append(t['id'])
popularity.append(t['popularity'])
explicit.append(t["explicit"])
year_song.append(int(j[-4:]))
seedIter = seedIter + 1
print('number of elements in the track_id list:', len(track_id))
#imports track names
import pandas as pd
df_tracks = pd.DataFrame({'artist_name':artist_name,
'track_name':track_name,
'track_id':track_id,
'popularity':popularity,
'explicit':explicit,
'year_song':year_song})
print(df_tracks.shape)
len(df_tracks)
number of elements in the track_id list: 60000 (60000, 6)
60000
#SONG NAME DATA CLEANUP
grouped = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped[grouped > 1].count()
df_tracks.drop_duplicates(subset=['artist_name','track_name'], inplace=True)
# doing the same grouping as before to verify the solution
grouped_after_dropping = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped_after_dropping[grouped_after_dropping > 1].count()
df_tracks[df_tracks.duplicated(subset=['artist_name','track_name'],keep=False)].count()
df_tracks.shape
(56653, 6)
#GET SONG METRICS OF THE RANDOM SAMPLE
# empty list, batchsize and the counter for None results
rows = []
batchsize = 50
None_counter = 0
for i in range(0,len(df_tracks['track_id']),batchsize):
batch = df_tracks['track_id'][i:i+batchsize]
feature_results = sp.audio_features(batch)
for i, t in enumerate(feature_results):
if t == None:
None_counter = None_counter + 1
else:
rows.append(t)
print('Number of tracks where no audio features were available:',None_counter)
print('number of elements in the track_id list:', len(rows))
df_audio_features = pd.DataFrame.from_dict(rows,orient='columns')
print("Shape of the dataset:", df_audio_features.shape)
df_audio_features.head()
df_audio_features.info()
Number of tracks where no audio features were available: 1 number of elements in the track_id list: 56652 Shape of the dataset: (56652, 18) <class 'pandas.core.frame.DataFrame'> RangeIndex: 56652 entries, 0 to 56651 Data columns (total 18 columns): acousticness 56652 non-null float64 analysis_url 56652 non-null object danceability 56652 non-null float64 duration_ms 56652 non-null int64 energy 56652 non-null float64 id 56652 non-null object instrumentalness 56652 non-null float64 key 56652 non-null int64 liveness 56652 non-null float64 loudness 56652 non-null float64 mode 56652 non-null int64 speechiness 56652 non-null float64 tempo 56652 non-null float64 time_signature 56652 non-null int64 track_href 56652 non-null object type 56652 non-null object uri 56652 non-null object valence 56652 non-null float64 dtypes: float64(9), int64(4), object(5) memory usage: 7.8+ MB
#SONG METRICS DATA CLEANUP
columns_to_drop = ['analysis_url','track_href','type','uri']
df_audio_features.drop(columns_to_drop, axis=1,inplace=True)
df_audio_features.rename(columns={'id': 'track_id'}, inplace=True)
df_audio_features.shape
(56652, 14)
# MERGE BOTH DATAFRAMES
# the 'inner' method will make sure that we only keep track IDs present in both datasets
df = pd.merge(df_tracks,df_audio_features,on='track_id',how='inner')
print("Shape of the dataset:", df_audio_features.shape)
df.head()
df.info()
df[df.duplicated(subset=['artist_name','track_name'],keep=False)]
Shape of the dataset: (56652, 14) <class 'pandas.core.frame.DataFrame'> Int64Index: 56652 entries, 0 to 56651 Data columns (total 19 columns): artist_name 56652 non-null object track_name 56652 non-null object track_id 56652 non-null object popularity 56652 non-null int64 explicit 56652 non-null bool year_song 56652 non-null int64 acousticness 56652 non-null float64 danceability 56652 non-null float64 duration_ms 56652 non-null int64 energy 56652 non-null float64 instrumentalness 56652 non-null float64 key 56652 non-null int64 liveness 56652 non-null float64 loudness 56652 non-null float64 mode 56652 non-null int64 speechiness 56652 non-null float64 tempo 56652 non-null float64 time_signature 56652 non-null int64 valence 56652 non-null float64 dtypes: bool(1), float64(9), int64(6), object(3) memory usage: 8.3+ MB
| artist_name | track_name | track_id | popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence |
|---|
#WRITE TO CSV
df.to_csv('../csv_files/datasetYearRandom.csv')
dfYearRandom = df
dfYearRandom.head()
| artist_name | track_name | track_id | popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Jason Mraz | I'm Yours | 1EzrEOXmMH3G43AXT1y7pA | 82 | False | 2008 | 0.59500 | 0.686 | 242187 | 0.457 | 0.0 | 11 | 0.105 | -8.322 | 1 | 0.0468 | 150.953 | 4 | 0.718 |
| 1 | Zac Brown Band | Chicken Fried | 4dGJf1SER1T6ooX46vwzRB | 75 | False | 2008 | 0.64500 | 0.566 | 238147 | 0.713 | 0.0 | 6 | 0.114 | -4.250 | 1 | 0.0417 | 169.864 | 4 | 0.807 |
| 2 | Estelle | American Boy | 22UDw8rSfLbUsaAGTXQ4Z8 | 79 | True | 2008 | 0.17100 | 0.727 | 284733 | 0.729 | 0.0 | 0 | 0.070 | -2.990 | 1 | 0.3260 | 117.932 | 4 | 0.512 |
| 3 | Kanye West | Heartless | 4EWCNWgDS8707fNSZ1oaA5 | 75 | False | 2008 | 0.05150 | 0.790 | 211000 | 0.647 | 0.0 | 10 | 0.248 | -5.983 | 0 | 0.1360 | 87.999 | 4 | 0.654 |
| 4 | The Offspring | You're Gonna Go Far, Kid | 6TfBA04WJ3X1d1wXhaCFVT | 77 | True | 2008 | 0.00428 | 0.550 | 177827 | 0.917 | 0.0 | 0 | 0.197 | -3.159 | 1 | 0.0638 | 126.115 | 4 | 0.601 |
#GET SONG NAMES OF RANDOM SAMPLE
#####################################
sample_size = 1000 # number of songs#
#####################################
random.seed(123)
# create empty lists where the results are going to be stored
artist_name = []
track_name = []
popularity = []
track_id = []
explicit = []
year_song = []
#random sample. set sample size, sample rate max 100, and song year
for i in range(0,sample_size,10):
track_results = sp.search(q='year:2019', type='track', limit=10,offset=i)
for i, t in enumerate(track_results['tracks']['items']):
artist_name.append(t['artists'][0]['name'])
track_name.append(t['name'])
track_id.append(t['id'])
popularity.append(t['popularity'])
explicit.append(t["explicit"])
year_song.append(int(j[-4:]))
print('number of elements in the track_id list:', len(track_id))
#imports track names
import pandas as pd
df_tracks = pd.DataFrame({'artist_name':artist_name,
'track_name':track_name,
'track_id':track_id,
'popularity':popularity,
'explicit':explicit,
'year_song':year_song})
print(df_tracks.shape)
df_tracks.head()
number of elements in the track_id list: 1000 (1000, 6)
| artist_name | track_name | track_id | popularity | explicit | year_song | |
|---|---|---|---|---|---|---|
| 0 | Roddy Ricch | The Box | 0nbXyq5TXYPCO7pr3N8S4I | 100 | True | 2019 |
| 1 | Arizona Zervas | ROXANNE | 696DnlkuDOXcMAnKlTgXXK | 99 | True | 2019 |
| 2 | Post Malone | Circles | 21jGcNKet2qwijlDFuPiPb | 97 | False | 2019 |
| 3 | DaBaby | BOP | 6Ozh9Ok6h4Oi1wUSLtBseN | 93 | True | 2019 |
| 4 | Juice WRLD | Bandit (with YoungBoy Never Broke Again) | 6Gg1gjgKi2AK4e0qzsR7sd | 92 | True | 2019 |
#SONG NAME DATA CLEANUP
grouped = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped[grouped > 1].count()
df_tracks.drop_duplicates(subset=['artist_name','track_name'], inplace=True)
# doing the same grouping as before to verify the solution
grouped_after_dropping = df_tracks.groupby(['artist_name','track_name'], as_index=True).size()
grouped_after_dropping[grouped_after_dropping > 1].count()
df_tracks[df_tracks.duplicated(subset=['artist_name','track_name'],keep=False)].count()
df_tracks.shape
(962, 6)
#GET SONG METRICS OF THE RANDOM SAMPLE
# empty list, batchsize and the counter for None results
rows = []
batchsize = 50
None_counter = 0
for i in range(0,len(df_tracks['track_id']),batchsize):
batch = df_tracks['track_id'][i:i+batchsize]
feature_results = sp.audio_features(batch)
for i, t in enumerate(feature_results):
if t == None:
None_counter = None_counter + 1
else:
rows.append(t)
print('Number of tracks where no audio features were available:',None_counter)
print('number of elements in the track_id list:', len(rows))
df_audio_features = pd.DataFrame.from_dict(rows,orient='columns')
print("Shape of the dataset:", df_audio_features.shape)
df_audio_features.head()
df_audio_features.info()
Number of tracks where no audio features were available: 0 number of elements in the track_id list: 962 Shape of the dataset: (962, 18) <class 'pandas.core.frame.DataFrame'> RangeIndex: 962 entries, 0 to 961 Data columns (total 18 columns): acousticness 962 non-null float64 analysis_url 962 non-null object danceability 962 non-null float64 duration_ms 962 non-null int64 energy 962 non-null float64 id 962 non-null object instrumentalness 962 non-null float64 key 962 non-null int64 liveness 962 non-null float64 loudness 962 non-null float64 mode 962 non-null int64 speechiness 962 non-null float64 tempo 962 non-null float64 time_signature 962 non-null int64 track_href 962 non-null object type 962 non-null object uri 962 non-null object valence 962 non-null float64 dtypes: float64(9), int64(4), object(5) memory usage: 135.4+ KB
#SONG METRICS DATA CLEANUP
columns_to_drop = ['analysis_url','track_href','type','uri']
df_audio_features.drop(columns_to_drop, axis=1,inplace=True)
df_audio_features.rename(columns={'id': 'track_id'}, inplace=True)
df_audio_features.shape
(962, 14)
# the 'inner' method will make sure that we only keep track IDs present in both datasets
dfTest = pd.merge(df_tracks,df_audio_features,on='track_id',how='inner')
print("Shape of the dataset:", df_audio_features.shape)
dfTest.head()
dfTest.info()
dfTest[dfTest.duplicated(subset=['artist_name','track_name'],keep=False)]
Shape of the dataset: (962, 14) <class 'pandas.core.frame.DataFrame'> Int64Index: 962 entries, 0 to 961 Data columns (total 19 columns): artist_name 962 non-null object track_name 962 non-null object track_id 962 non-null object popularity 962 non-null int64 explicit 962 non-null bool year_song 962 non-null int64 acousticness 962 non-null float64 danceability 962 non-null float64 duration_ms 962 non-null int64 energy 962 non-null float64 instrumentalness 962 non-null float64 key 962 non-null int64 liveness 962 non-null float64 loudness 962 non-null float64 mode 962 non-null int64 speechiness 962 non-null float64 tempo 962 non-null float64 time_signature 962 non-null int64 valence 962 non-null float64 dtypes: bool(1), float64(9), int64(6), object(3) memory usage: 143.7+ KB
| artist_name | track_name | track_id | popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence |
|---|
#WRITE TO CSV
dfTest.to_csv('../csv_files/datasetTestYearRandom.csv')
dfTestYearRandom = dfTest
dfTestYearRandom.head()
| artist_name | track_name | track_id | popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Roddy Ricch | The Box | 0nbXyq5TXYPCO7pr3N8S4I | 100 | True | 2019 | 0.1040 | 0.896 | 196653 | 0.586 | 0.00000 | 10 | 0.7900 | -6.687 | 0 | 0.0559 | 116.971 | 4 | 0.642 |
| 1 | Arizona Zervas | ROXANNE | 696DnlkuDOXcMAnKlTgXXK | 99 | True | 2019 | 0.0522 | 0.621 | 163636 | 0.601 | 0.00000 | 6 | 0.4600 | -5.616 | 0 | 0.1480 | 116.735 | 5 | 0.457 |
| 2 | Post Malone | Circles | 21jGcNKet2qwijlDFuPiPb | 97 | False | 2019 | 0.1920 | 0.695 | 215280 | 0.762 | 0.00244 | 0 | 0.0863 | -3.497 | 1 | 0.0395 | 120.042 | 4 | 0.553 |
| 3 | DaBaby | BOP | 6Ozh9Ok6h4Oi1wUSLtBseN | 93 | True | 2019 | 0.1890 | 0.769 | 159715 | 0.787 | 0.00000 | 11 | 0.1290 | -3.909 | 1 | 0.3670 | 126.770 | 4 | 0.836 |
| 4 | Juice WRLD | Bandit (with YoungBoy Never Broke Again) | 6Gg1gjgKi2AK4e0qzsR7sd | 92 | True | 2019 | 0.0687 | 0.474 | 189323 | 0.631 | 0.00000 | 5 | 0.1320 | -5.884 | 0 | 0.3430 | 180.051 | 4 | 0.425 |
dfTraining = dfYearRandom
# CHOOSE:
# dfYearRandom
# dfPlaylist
# dfTraining
dfEncodedTarget = dfTraining
bins = [0,10,20,30,40,50,60,70,80,90,100]
labels=[0,1,2,3,4,5,6,7,8,9]
dfEncodedTarget['popularity'] = pd.cut(dfEncodedTarget['popularity'], bins=bins, labels=labels, include_lowest=True)
dfEncodedTarget.head()
| artist_name | track_name | track_id | popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Jason Mraz | I'm Yours | 1EzrEOXmMH3G43AXT1y7pA | 8 | False | 2008 | 0.59500 | 0.686 | 242187 | 0.457 | 0.0 | 11 | 0.105 | -8.322 | 1 | 0.0468 | 150.953 | 4 | 0.718 |
| 1 | Zac Brown Band | Chicken Fried | 4dGJf1SER1T6ooX46vwzRB | 7 | False | 2008 | 0.64500 | 0.566 | 238147 | 0.713 | 0.0 | 6 | 0.114 | -4.250 | 1 | 0.0417 | 169.864 | 4 | 0.807 |
| 2 | Estelle | American Boy | 22UDw8rSfLbUsaAGTXQ4Z8 | 7 | True | 2008 | 0.17100 | 0.727 | 284733 | 0.729 | 0.0 | 0 | 0.070 | -2.990 | 1 | 0.3260 | 117.932 | 4 | 0.512 |
| 3 | Kanye West | Heartless | 4EWCNWgDS8707fNSZ1oaA5 | 7 | False | 2008 | 0.05150 | 0.790 | 211000 | 0.647 | 0.0 | 10 | 0.248 | -5.983 | 0 | 0.1360 | 87.999 | 4 | 0.654 |
| 4 | The Offspring | You're Gonna Go Far, Kid | 6TfBA04WJ3X1d1wXhaCFVT | 7 | True | 2008 | 0.00428 | 0.550 | 177827 | 0.917 | 0.0 | 0 | 0.197 | -3.159 | 1 | 0.0638 | 126.115 | 4 | 0.601 |
popularityPercentage = dfEncodedTarget.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()
tablePopularity
popularity 0 0.000000 1 0.000000 2 0.000459 3 0.155776 4 0.329150 5 0.308374 6 0.160100 7 0.039293 8 0.006266 9 0.000583 Name: popularity, dtype: float64
tablePopularity.plot.bar()
plt.show()
bins = [0,10,20,30,40,50,60,70,80,90,100]
labels=[0,1,2,3,4,5,6,7,8,9]
len(bins),len(labels)
(11, 10)
bins = [0,10,20,30,40,50,60,70,80,90,100]
labels=[0,1,2,3,4,5,6,7,8,9]
dfTestYearRandom['popularity'] = pd.cut(dfTestYearRandom['popularity'], bins=bins, labels=labels, include_lowest=True)
dfTestYearRandom.head()
| artist_name | track_name | track_id | popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Roddy Ricch | The Box | 0nbXyq5TXYPCO7pr3N8S4I | 9 | True | 2019 | 0.1040 | 0.896 | 196653 | 0.586 | 0.00000 | 10 | 0.7900 | -6.687 | 0 | 0.0559 | 116.971 | 4 | 0.642 |
| 1 | Arizona Zervas | ROXANNE | 696DnlkuDOXcMAnKlTgXXK | 9 | True | 2019 | 0.0522 | 0.621 | 163636 | 0.601 | 0.00000 | 6 | 0.4600 | -5.616 | 0 | 0.1480 | 116.735 | 5 | 0.457 |
| 2 | Post Malone | Circles | 21jGcNKet2qwijlDFuPiPb | 9 | False | 2019 | 0.1920 | 0.695 | 215280 | 0.762 | 0.00244 | 0 | 0.0863 | -3.497 | 1 | 0.0395 | 120.042 | 4 | 0.553 |
| 3 | DaBaby | BOP | 6Ozh9Ok6h4Oi1wUSLtBseN | 9 | True | 2019 | 0.1890 | 0.769 | 159715 | 0.787 | 0.00000 | 11 | 0.1290 | -3.909 | 1 | 0.3670 | 126.770 | 4 | 0.836 |
| 4 | Juice WRLD | Bandit (with YoungBoy Never Broke Again) | 6Gg1gjgKi2AK4e0qzsR7sd | 9 | True | 2019 | 0.0687 | 0.474 | 189323 | 0.631 | 0.00000 | 5 | 0.1320 | -5.884 | 0 | 0.3430 | 180.051 | 4 | 0.425 |
popularityPercentage = dfTestYearRandom.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()
tablePopularity.plot.bar()
plt.show()
len(dfTestYearRandom)/(len(dfTraining)+len(dfTestYearRandom))
0.0166973305099455
Given that the API only provide a specific sample of songs, the levels of popularity is limited; so, we need this adjustment to make every model work.
len(bins), len(labels)
(11, 10)
bins = range(min(dfEncodedTarget['popularity']),max(dfEncodedTarget['popularity'])+1)
labels = []
for i in bins[:-1]:
labels.append(i- min(dfEncodedTarget['popularity']))
dfEncodedTarget['popularity'] = pd.cut(dfEncodedTarget['popularity'], bins=bins, labels=labels, include_lowest=True)
dfEncodedTarget.head()
| artist_name | track_name | track_id | popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Jason Mraz | I'm Yours | 1EzrEOXmMH3G43AXT1y7pA | 5 | False | 2008 | 0.59500 | 0.686 | 242187 | 0.457 | 0.0 | 11 | 0.105 | -8.322 | 1 | 0.0468 | 150.953 | 4 | 0.718 |
| 1 | Zac Brown Band | Chicken Fried | 4dGJf1SER1T6ooX46vwzRB | 4 | False | 2008 | 0.64500 | 0.566 | 238147 | 0.713 | 0.0 | 6 | 0.114 | -4.250 | 1 | 0.0417 | 169.864 | 4 | 0.807 |
| 2 | Estelle | American Boy | 22UDw8rSfLbUsaAGTXQ4Z8 | 4 | True | 2008 | 0.17100 | 0.727 | 284733 | 0.729 | 0.0 | 0 | 0.070 | -2.990 | 1 | 0.3260 | 117.932 | 4 | 0.512 |
| 3 | Kanye West | Heartless | 4EWCNWgDS8707fNSZ1oaA5 | 4 | False | 2008 | 0.05150 | 0.790 | 211000 | 0.647 | 0.0 | 10 | 0.248 | -5.983 | 0 | 0.1360 | 87.999 | 4 | 0.654 |
| 4 | The Offspring | You're Gonna Go Far, Kid | 6TfBA04WJ3X1d1wXhaCFVT | 4 | True | 2008 | 0.00428 | 0.550 | 177827 | 0.917 | 0.0 | 0 | 0.197 | -3.159 | 1 | 0.0638 | 126.115 | 4 | 0.601 |
msk = np.random.rand(len(dfEncodedTarget)) < 0.8
dfEncodedTarget = df[msk]
dfTestYearRandom = df[~msk]
len(dfTestYearRandom)/(len(dfEncodedTarget)+len(dfTestYearRandom))
0.1975040598743204
popularityPercentage = dfEncodedTarget.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()
tablePopularity.plot.bar()
plt.show()
tablePopularity.mean()
0.14285714285714285
popularityPercentage = dfTestYearRandom.groupby('popularity')
tablePopularity = popularityPercentage["popularity"].count() / popularityPercentage["popularity"].count().sum()
tablePopularity.plot.bar()
plt.show()
dfEncoded = dfEncodedTarget
dfEncoded['explicit'] = dfEncoded['explicit'].map(lambda x: 0 if x == False else 1)
/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
dfEncoded = dfEncoded.iloc[:,3:]
dfEncoded.head()
| popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5 | 0 | 2008 | 0.59500 | 0.686 | 242187 | 0.457 | 0.0 | 11 | 0.1050 | -8.322 | 1 | 0.0468 | 150.953 | 4 | 0.718 |
| 1 | 4 | 0 | 2008 | 0.64500 | 0.566 | 238147 | 0.713 | 0.0 | 6 | 0.1140 | -4.250 | 1 | 0.0417 | 169.864 | 4 | 0.807 |
| 2 | 4 | 1 | 2008 | 0.17100 | 0.727 | 284733 | 0.729 | 0.0 | 0 | 0.0700 | -2.990 | 1 | 0.3260 | 117.932 | 4 | 0.512 |
| 4 | 4 | 1 | 2008 | 0.00428 | 0.550 | 177827 | 0.917 | 0.0 | 0 | 0.1970 | -3.159 | 1 | 0.0638 | 126.115 | 4 | 0.601 |
| 5 | 4 | 0 | 2008 | 0.27200 | 0.508 | 261640 | 0.720 | 0.0 | 11 | 0.0563 | -5.908 | 0 | 0.0628 | 79.983 | 4 | 0.472 |
dfEncodedTest = dfTestYearRandom
dfEncodedTest['explicit'] = dfEncodedTest['explicit'].map(lambda x: 0 if x == False else 1)
dfEncodedTest = dfEncodedTest.iloc[:,3:]
dfEncodedTest.head()
/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
| popularity | explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 4 | 0 | 2008 | 0.0515 | 0.790 | 211000 | 0.647 | 0.000000 | 10 | 0.2480 | -5.983 | 0 | 0.1360 | 87.999 | 4 | 0.654 |
| 6 | 4 | 0 | 2008 | 0.0941 | 0.485 | 242373 | 0.619 | 0.000003 | 5 | 0.1090 | -7.115 | 0 | 0.0289 | 138.017 | 4 | 0.416 |
| 7 | 4 | 0 | 2008 | 0.1730 | 0.630 | 261427 | 0.698 | 0.000000 | 10 | 0.0789 | -4.510 | 1 | 0.0244 | 94.023 | 4 | 0.529 |
| 10 | 4 | 1 | 2008 | 0.0387 | 0.673 | 221840 | 0.695 | 0.002060 | 6 | 0.1940 | -8.636 | 0 | 0.2780 | 151.468 | 4 | 0.774 |
| 11 | 4 | 1 | 2008 | 0.0161 | 0.680 | 249533 | 0.687 | 0.000000 | 9 | 0.2610 | -6.162 | 0 | 0.0709 | 150.053 | 4 | 0.467 |
# INPUT: dfEncoded
# OUTPUT: predictiveVariablesScaled data scaled by MinMaxScaler to prepare data for the Lasso
# popularityTarget
scaler = MinMaxScaler()
predictiveVariablesScaled = pd.DataFrame(scaler.fit_transform(dfEncoded.iloc[:,1:]), columns=dfEncoded.iloc[:,1:].columns)
popularityTarget = dfEncoded['popularity']
predictiveVariablesScaled.head()
| explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.597390 | 0.695740 | 0.043541 | 0.456989 | 0.0 | 1.000000 | 0.1050 | 0.824296 | 1.0 | 0.048750 | 0.648730 | 0.8 | 0.720160 |
| 1 | 0.0 | 0.0 | 0.647590 | 0.574037 | 0.042792 | 0.712994 | 0.0 | 0.545455 | 0.1140 | 0.896456 | 1.0 | 0.043438 | 0.730001 | 0.8 | 0.809428 |
| 2 | 1.0 | 0.0 | 0.171687 | 0.737323 | 0.051425 | 0.728995 | 0.0 | 0.000000 | 0.0700 | 0.918784 | 1.0 | 0.339583 | 0.506820 | 0.8 | 0.513541 |
| 3 | 1.0 | 0.0 | 0.004297 | 0.557809 | 0.031614 | 0.916998 | 0.0 | 0.000000 | 0.1970 | 0.915789 | 1.0 | 0.066458 | 0.541987 | 0.8 | 0.602808 |
| 4 | 0.0 | 0.0 | 0.273092 | 0.515213 | 0.047146 | 0.719994 | 0.0 | 1.000000 | 0.0563 | 0.867074 | 0.0 | 0.065417 | 0.343732 | 0.8 | 0.473420 |
# INPUT: dfEncoded
# OUTPUT: predictiveVariablesScaled data scaled by MinMaxScaler to prepare data for the Lasso
# popularityTarget
scaler = MinMaxScaler()
predictiveVariablesScaledTest = pd.DataFrame(scaler.fit_transform(dfEncodedTest.iloc[:,1:]), columns=dfEncodedTest.iloc[:,1:].columns)
popularityTargetTest = dfEncodedTest['popularity']
predictiveVariablesScaledTest.head()
| explicit | year_song | acousticness | danceability | duration_ms | energy | instrumentalness | key | liveness | loudness | mode | speechiness | tempo | time_signature | valence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.051707 | 0.800405 | 0.044181 | 0.647641 | 0.000000 | 0.909091 | 0.251777 | 0.882984 | 0.0 | 0.140787 | 0.401216 | 0.8 | 0.659274 |
| 1 | 0.0 | 0.0 | 0.094478 | 0.491388 | 0.051852 | 0.619612 | 0.000003 | 0.454545 | 0.110660 | 0.862956 | 0.0 | 0.029917 | 0.629264 | 0.8 | 0.419355 |
| 2 | 0.0 | 0.0 | 0.173695 | 0.638298 | 0.056511 | 0.698693 | 0.000000 | 0.909091 | 0.080102 | 0.909044 | 1.0 | 0.025259 | 0.428681 | 0.8 | 0.533266 |
| 3 | 1.0 | 0.0 | 0.038855 | 0.681864 | 0.046832 | 0.695690 | 0.002060 | 0.545455 | 0.196954 | 0.836046 | 0.0 | 0.287785 | 0.690591 | 0.8 | 0.780242 |
| 4 | 1.0 | 0.0 | 0.016165 | 0.688956 | 0.053603 | 0.687682 | 0.000000 | 0.818182 | 0.264975 | 0.879817 | 0.0 | 0.073395 | 0.684139 | 0.8 | 0.470766 |
What LASSO does well is to provide a principled way to reduce the number of features in a model. LASSO involves a penalty factor that determines how many features are retained; using cross-validation to choose the penalty factor helps assure that the model will generalize well to future data samples.
If you need to cut down on the number of predictors for practical reasons, LASSO is a good choice. But all it does is give you a useful set of selected predictors, not necessarily the most important in some general sense.
When features are correlated, LASSO will choose one or the other based on its performance in the particular data sample at hand. With a different sample it could well choose a different feature from a set of correlated features.
This is why we are interested in using Lasso for the complete data set.
# INPUT: predictiveVariablesScaled, variables scaled and prepared for the Lasso and popularityTarget
# OUTPUT:
# 'cs',the array of penalties for the Lasso method
# depending on the variables selected.
start_time = time.time()
cs = l1_min_c(predictiveVariablesScaled, popularityTarget, loss='log') * np.logspace(0, 7, 16)
#We use the Logistic Regression to apply the penalties from the Lasso.
clf = linear_model.LogisticRegression(penalty='l1', solver='saga',
tol=1e-6, max_iter=int(1e6),
warm_start=True,
multi_class= 'multinomial')
print("--- %s seconds ---" % (time.time() - start_time))
--- 0.01909017562866211 seconds ---
# ************************************************
# ATTENTION!!!! 763.1 s Executing time!!*
# ************************************************
# INPUT: predictiveVariables, variables scaled and prepared for the Lasso and popularityTarget
# OUTPUT: fit the linear model to minimize the error function
# for graphical purpose and see how Lasso selects the variables.
start_time = time.time()
coefs_ = []
for c in cs:
clf.set_params(C=c)
clf.fit(X = predictiveVariablesScaled, y = popularityTarget)
coefs_.append(clf.coef_.ravel().copy())
print("--- %s seconds ---" % (time.time() - start_time))
--- 763.1988909244537 seconds ---
coefs_ = np.array(coefs_)
plt.plot(np.log10(cs), coefs_, marker='o')
ymin, ymax = plt.ylim()
plt.xlabel('log(C)')
plt.ylabel('Coefficients')
plt.title('Logistic Regression Path')
plt.axis('tight')
plt.show()
# INPUT: predictiveVariablesScaled and clf, the linear_model.LogisticRegression
# OUTPUT: lassoVariablesFilter, it has all the variables with its corresponding coefficient given by the Lasso.
# If the coefficient == o, it means it shouldn't be included in the model.
start_time = time.time()
lassoVariablesFilter = []
# for coefs in range(0,clf.coef_.size):
for coefs in range(0,predictiveVariablesScaled.shape[1]):
lassoVariablesFilter.append((predictiveVariablesScaled.columns[coefs], clf.coef_[0][coefs]))
print("--- %s seconds ---" % (time.time() - start_time))
--- 0.0003440380096435547 seconds ---
# INPUT:
# lassoVariablesFilter, , it has all the variables with its corresponding coefficient given by the Lasso.
# lassoVars,data of all the variables scaled by MinMaxScaler to prepare data for the Lasso
# numericaAndCategoricalVariablesDataframe, dataframe with all variables.
# OUTPUT: lassoVariablesSelectedDataFrame, data frame with the data of the variables selected by the Lasso and scaled.
# lassoVariablesSelectedNonScaledDataFrame, , data frame with the data of the variables selected by the Lasso.
start_time = time.time()
out_tup = [i for i in lassoVariablesFilter if i[1] == 0]
lassoVariablesSelected = [i for i in lassoVariablesFilter if i[1] != 0]
lassoVariablesSelectedDataFrame = predictiveVariablesScaled[list(map(lambda x: x[0] , lassoVariablesSelected))]
lassoVariablesSelectedNonScaledDataFrame = dfEncoded.iloc[:,1:][list(map(lambda x: x[0] , lassoVariablesSelected))]
print("--- %s seconds ---" % (time.time() - start_time))
--- 0.01439213752746582 seconds ---
All variables are important based on the Lasso model.
lassoVariablesSelected, len(lassoVariablesSelected)
([('explicit', -0.07153324599907189),
('year_song', -9.702644827469165),
('acousticness', -0.3254471074277977),
('danceability', -1.2073589518844228),
('duration_ms', -2.8156027992892434),
('energy', 0.01184728627651793),
('instrumentalness', 0.3899812786757341),
('key', 0.041241030799594784),
('liveness', 0.25354062649132436),
('loudness', -0.6421474270361321),
('mode', 0.24023488849848687),
('speechiness', 1.9882322610031846),
('tempo', -0.08274000034546936),
('time_signature', -0.4471910261268723),
('valence', -0.008273612271184968)],
15)
The regularization of the Lasso model seems to consider all the variables as important, although 'year_song' seems to be much more important than the rest. Here we see for the first time that 'year_song' damages the objective of the study because its objective is to extract the characteristics of the songs to advise the artists or the company, and there is nothing to do about the year.
| Feature | Description | |
|---|---|---|
| artist_name | The artists who performed the track. Each artist object includes a link in href to more detailed information about the artist. | |
| track_name | The name of the track. | |
| track_id | The Spotify ID for the track. | |
| explicit | Whether or not the track has explicit lyrics ( true = yes it does; false = no it does not OR unknown). | |
| acousticness | A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic. | |
| danceability | Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. | |
| duration_ms_y | The duration of the track in milliseconds. | |
| energy | Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. | |
| instrumentalness | Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. | |
| key | The estimated overall key of the track. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. | |
| liveness | Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. | |
| loudness | The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db. | |
| mode | Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0. | |
| speechiness | Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. | |
| tempo | The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. | |
| time_signature | An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure). | |
| valence | A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). |
| Feature | Description |
|---|---|
| popularity | The popularity of the track. The value will be between 0 and 100, with 100 being the most popular. The popularity of a track is a value between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Generally speaking, songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past. |
def edaCategorical2 (var="explicit", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table"):
varDataFrame = pd.DataFrame(df[str(var)])
classifierDataFrame = pd.DataFrame(dfEncoded[str(classifier)])
varClassifierDataFrame = pd.concat([varDataFrame, classifierDataFrame], axis=1, sort=False)
#Contingency table
data_crosstab = pd.crosstab(varClassifierDataFrame[str(var)],
varClassifierDataFrame[str(classifier)],
margins = False)
p = plt.figure()
#Histogram based on type
varClassifierDataFrame1 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 1]
varClassifierDataFrame2 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 2]
varClassifierDataFrame3 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 3]
varClassifierDataFrame4 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 4]
varClassifierDataFrame5 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 5]
varClassifierDataFrame6 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 6]
varClassifierDataFrame7 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 7]
varClassifierDataFrame8 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 8]
varClassifierDataFrame9 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 9]
varClassifierDataFrame10 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 10]
# Method 1: on the same Axis
sns.distplot( varClassifierDataFrame1[str(var)] , color="skyblue", label="1")
sns.distplot( varClassifierDataFrame2[str(var)] , color="red", label="2")
sns.distplot( varClassifierDataFrame3[str(var)] , color="blue", label="3")
sns.distplot( varClassifierDataFrame4[str(var)] , color="green", label="4")
sns.distplot( varClassifierDataFrame5[str(var)] , color="black", label="5")
sns.distplot( varClassifierDataFrame6[str(var)] , color="gold", label="6")
sns.distplot( varClassifierDataFrame7[str(var)] , color="grey", label="7")
sns.distplot( varClassifierDataFrame8[str(var)] , color="purple", label="8")
sns.distplot( varClassifierDataFrame9[str(var)] , color="yellow", label="9")
sns.distplot( varClassifierDataFrame10[str(var)] , color="orange", label="10")
plt.legend()
#Global histogram
# Cut the window in 2 parts
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
# Add a graph in each part
sns.boxplot(varClassifierDataFrame[str(var)], ax=ax_box)
sns.distplot(varClassifierDataFrame[str(var)], ax=ax_hist)
# Remove x axis name for the boxplot
ax_box.set(xlabel='')
plt.close()
return f
def edaNumeric2 (var, classifier= "popularity", df=dfEncoded, wShowing = "BoxPLot"):
varDataFrame = pd.DataFrame(df[str(var)])
classifierDataFrame = pd.DataFrame(dfEncoded[str(classifier)])
varClassifierDataFrame = pd.concat([varDataFrame, classifierDataFrame], axis=1, sort=False)
b = plt.figure()
#Boxplot
ax = sns.boxplot(x= str(classifier), y= str(var), data=varClassifierDataFrame)
# Calculate number of obs per group & median to position labels
medians = varClassifierDataFrame.groupby([str(classifier)])[str(var)].median().values
nobs = varClassifierDataFrame[str(var)].value_counts().values
nobs = [str(x) for x in nobs.tolist()]
nobs = ["n: " + i for i in nobs]
# Add it to the plot
pos = range(len(nobs))
for tick,label in zip(pos,ax.get_xticklabels()):
ax.text(pos[tick], medians[tick] + 0.03, nobs[tick],
horizontalalignment='center', size='x-small', color='w', weight='semibold')
# plt.close()
h = plt.figure()
#Histogram based on type
varClassifierDataFrame1 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 1]
varClassifierDataFrame2 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 2]
varClassifierDataFrame3 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 3]
varClassifierDataFrame4 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 4]
varClassifierDataFrame5 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 5]
varClassifierDataFrame6 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 6]
varClassifierDataFrame7 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 7]
varClassifierDataFrame8 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 8]
varClassifierDataFrame9 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 9]
varClassifierDataFrame10 = varClassifierDataFrame[varClassifierDataFrame[str(classifier)] == 10]
# Method 1: on the same Axis
sns.distplot( varClassifierDataFrame1[str(var)] , color="skyblue", label="1")
sns.distplot( varClassifierDataFrame2[str(var)] , color="red", label="2")
sns.distplot( varClassifierDataFrame3[str(var)] , color="blue", label="3")
sns.distplot( varClassifierDataFrame4[str(var)] , color="green", label="4")
sns.distplot( varClassifierDataFrame5[str(var)] , color="black", label="5")
sns.distplot( varClassifierDataFrame6[str(var)] , color="gold", label="6")
sns.distplot( varClassifierDataFrame7[str(var)] , color="grey", label="7")
sns.distplot( varClassifierDataFrame8[str(var)] , color="purple", label="8")
sns.distplot( varClassifierDataFrame9[str(var)] , color="yellow", label="9")
sns.distplot( varClassifierDataFrame10[str(var)] , color="orange", label="10")
plt.legend()
# plt.close()
plt.figure()
#Global histogram
# Cut the window in 2 parts
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
# Add a graph in each part
sns.boxplot(varClassifierDataFrame[str(var)], ax=ax_box)
sns.distplot(varClassifierDataFrame[str(var)], ax=ax_hist)
# Remove x axis name for the boxplot
ax_box.set(xlabel='')
plt.close()
return f
edaNumeric2 (var = 'danceability', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'energy', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'loudness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'speechiness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'acousticness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'instrumentalness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'liveness', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'valence', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'tempo', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaNumeric2 (var = 'duration_ms', classifier= "popularity", df = dfEncoded, wShowing = "BoxPLot")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
<Figure size 432x288 with 0 Axes>
edaCategorical2 (var="explicit", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
edaCategorical2 (var="key", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
edaCategorical2 (var="mode", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
edaCategorical2 (var="time_signature", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
edaCategorical2 (var="year_song", classifier = "popularity", df = dfEncoded, wShowing = "Contingency Table")
/Applications/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:908: RuntimeWarning: invalid value encountered in true_divide return n/db/n.sum(), bin_edges
corDf = dfEncoded.drop(['mode', 'explicit'], axis=1)
corDf['popularity'] = pd.to_numeric(corDf['popularity'])
# Compute the correlation matrix
corr = corDf.corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(13, 10))
# Generate a custom diverging colormap
# cmap = sns.diverging_palette(10, 220, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask,vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5});
We are going to evaluate each model with the following scores.
scorers = {
'precision_score': make_scorer(precision_score),
'recall_score': make_scorer(recall_score),
'accuracy_score': make_scorer(accuracy_score)
}
X_train = predictiveVariablesScaled.drop('year_song', axis = 1)
y_train = popularityTarget
X_test = predictiveVariablesScaledTest.drop('year_song', axis = 1)
y_test = popularityTargetTest
labelsMatrix = []
for i in labels:
labelsMatrix.append(str(i))
labelsMatrix
['0', '1', '2', '3', '4', '5', '6']
Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. We can call a Logistic Regression a Linear Regression model but the Logistic Regression uses a more complex cost function, this cost function can be defined as the ‘Sigmoid function’ or also known as the ‘logistic function’ instead of a linear function. The hypothesis of logistic regression tends it to limit the cost function between 0 and 1. Therefore linear functions fail to represent it as it can have a value greater than 1 or less than 0 which is not possible as per the hypothesis of logistic regression.
We expect our classifier to give us a set of outputs or classes based on probability when we pass the inputs through a prediction function and returns a probability score between 0 and 1. We decide with a threshold value above which we classify values into Class 1 and of the value goes below the threshold then we classify it in Class 2. The threshold is set by optimizaing (minimizing) a score (cost function); which in our case are the three scores mentioned at the begining of this report.

def print_cm(cm, labels, hide_zeroes=False, hide_diagonal=False, hide_threshold=None):
"""pretty print for confusion matrixes"""
columnwidth = max([len(x) for x in labels] + [5]) # 5 is value length
empty_cell = " " * columnwidth
# Print header
print(" " + empty_cell, end=" ")
for label in labels:
print("%{0}s".format(columnwidth) % label, end=" ")
print()
# Print rows
for i, label1 in enumerate(labels):
print(" %{0}s".format(columnwidth) % label1, end=" ")
for j in range(len(labels)):
cell = "%{0}.1f".format(columnwidth) % cm[i, j]
if hide_zeroes:
cell = cell if float(cm[i, j]) != 0 else empty_cell
if hide_diagonal:
cell = cell if i != j else empty_cell
if hide_threshold:
cell = cell if cm[i, j] > hide_threshold else empty_cell
print(cell, end=" ")
print()
lr = LogisticRegression(fit_intercept = False, C = 1e9, multi_class = 'multinomial', solver= 'newton-cg' )
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
# fit a model
lr = LogisticRegression(fit_intercept = False, C = 1e9);
model = lr.fit(X_train, y_train);
predictionsTest = lr.predict(X_test);
predictionsTrain = lr.predict(X_train);
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning) /Applications/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning. "this warning.", FutureWarning)
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 25.0 5033.0 2031.0 0.0 0.0 0.0 0.0
1 17.0 10052.0 4836.0 0.0 0.0 0.0 0.0
2 1.0 7487.0 6550.0 3.0 0.0 0.0 0.0
3 0.0 3393.0 3928.0 0.0 0.0 0.0 0.0
4 0.0 627.0 1155.0 0.0 0.0 0.0 0.0
5 0.0 93.0 206.0 0.0 0.0 0.0 0.0
6 0.0 2.0 24.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.58 0.00 0.01 7089
1 0.38 0.67 0.48 14905
2 0.35 0.47 0.40 14041
3 0.00 0.00 0.00 7321
4 0.00 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 0.00 0.00 0.00 26
accuracy 0.37 45463
macro avg 0.19 0.16 0.13 45463
weighted avg 0.32 0.37 0.28 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsTest))
Confusion matrix:
0 1 2 3 4 5 6
0 8.0 1360.0 394.0 0.0 0.0 0.0 0.0
1 4.0 2748.0 990.0 0.0 0.0 0.0 0.0
2 1.0 2059.0 1367.0 2.0 0.0 0.0 0.0
3 1.0 919.0 829.0 0.0 0.0 0.0 0.0
4 0.0 179.0 265.0 0.0 0.0 0.0 0.0
5 0.0 17.0 39.0 0.0 0.0 0.0 0.0
6 0.0 2.0 5.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.57 0.00 0.01 1762
1 0.38 0.73 0.50 3742
2 0.35 0.40 0.37 3429
3 0.00 0.00 0.00 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.37 11189
macro avg 0.19 0.16 0.13 11189
weighted avg 0.32 0.37 0.28 11189
features = X_train.columns
importances = np.abs(np.array(np.std(X_train)) * model.coef_)
indices = np.argsort(importances)
plt.title('Feature Importances')
plt.barh(range(len(indices[0])), importances[0][indices[0]], color='b', align='center')
plt.yticks(range(len(indices[0])), [features[i] for i in indices[0]])
plt.xlabel('Relative Importance')
plt.show()
lista = []
for i in indices[0]:
lista.append(features[i])
featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
| Feature Importance | |
|---|---|
| 0 | danceability |
| 1 | explicit |
| 2 | valence |
| 3 | energy |
| 4 | loudness |
| 5 | speechiness |
| 6 | instrumentalness |
| 7 | mode |
| 8 | duration_ms |
| 9 | liveness |
| 10 | time_signature |
| 11 | acousticness |
| 12 | tempo |
| 13 | key |
features = X_train.columns
#features = iris['feature_names']
importances = np.array(np.std(X_train)) * model.coef_
indices = np.argsort(importances)
plt.title('Feature Importances')
plt.barh(range(len(indices[0])), importances[0][indices[0]], color='b', align='center')
plt.yticks(range(len(indices[0])), [features[i] for i in indices[0]])
plt.xlabel('Relative Importance')
plt.show()
lista = []
for i in indices[0]:
lista.append(features[i])
featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf.head(10)
| Feature Importance | |
|---|---|
| 0 | valence |
| 1 | energy |
| 2 | speechiness |
| 3 | mode |
| 4 | duration_ms |
| 5 | liveness |
| 6 | key |
| 7 | tempo |
| 8 | acousticness |
| 9 | time_signature |
featureImportancedf.head(10).iloc[::-1]
| Feature Importance | |
|---|---|
| 9 | time_signature |
| 8 | acousticness |
| 7 | tempo |
| 6 | key |
| 5 | liveness |
| 4 | duration_ms |
| 3 | mode |
| 2 | speechiness |
| 1 | energy |
| 0 | valence |
The plan is to use different and more complex models each time in order to improve the prediction (i.e. the scores and results).
The next model used is Support Vector Machine. SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.
According to the SVM algorithm we find the points closest to the line from both classes. These points are called support vectors. We compute the distance between the line and the support vectors, this distance is called the margin. Our goal is to maximize the margin. The hyperplane for which the margin is maximum is the optimal hyperplane.

In machine learning, a “kernel” is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem. It entails transforming linearly inseparable data to linearly separable ones. The kernel function is what is applied on each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable.

clf = svm.SVC()
modelSvm = clf.fit(X_train, y_train)
predictionsSvm = clf.predict(X_test)
linear_svc = svm.SVC(kernel='linear')
linear_svc = linear_svc.fit(X_train, y_train)
linear_svc.kernel
predictionsSvmTest = linear_svc.predict(X_test)
predictionsSvmTrain = linear_svc.predict(X_train)
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning. "avoid this warning.", FutureWarning)
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsSvmTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsSvmTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 6086.0 1003.0 0.0 0.0 0.0 0.0
1 0.0 12322.0 2583.0 0.0 0.0 0.0 0.0
2 0.0 10470.0 3571.0 0.0 0.0 0.0 0.0
3 0.0 5138.0 2183.0 0.0 0.0 0.0 0.0
4 0.0 1101.0 681.0 0.0 0.0 0.0 0.0
5 0.0 189.0 110.0 0.0 0.0 0.0 0.0
6 0.0 14.0 12.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 7089
1 0.35 0.83 0.49 14905
2 0.35 0.25 0.30 14041
3 0.00 0.00 0.00 7321
4 0.00 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 0.00 0.00 0.00 26
accuracy 0.35 45463
macro avg 0.10 0.15 0.11 45463
weighted avg 0.22 0.35 0.25 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsSvmTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsSvmTest))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 1513.0 249.0 0.0 0.0 0.0 0.0
1 0.0 3157.0 585.0 0.0 0.0 0.0 0.0
2 0.0 2575.0 854.0 0.0 0.0 0.0 0.0
3 0.0 1218.0 531.0 0.0 0.0 0.0 0.0
4 0.0 288.0 156.0 0.0 0.0 0.0 0.0
5 0.0 31.0 25.0 0.0 0.0 0.0 0.0
6 0.0 5.0 2.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 1762
1 0.36 0.84 0.50 3742
2 0.36 0.25 0.29 3429
3 0.00 0.00 0.00 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.36 11189
macro avg 0.10 0.16 0.11 11189
weighted avg 0.23 0.36 0.26 11189
def f_importances(coef, names):
imp = coef
imp,names = zip(*sorted(zip(imp,names)))
plt.barh(range(len(names)), imp, align='center')
plt.yticks(range(len(names)), names)
plt.show()
features_names = [X_train.columns]
f_importances(abs(linear_svc.coef_[0]), list(features_names[0]))
lista = []
importances = abs(linear_svc.coef_)
indices = np.argsort(importances)
for i in indices[0]:
lista.append(features[i])
featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
| Feature Importance | |
|---|---|
| 0 | loudness |
| 1 | explicit |
| 2 | time_signature |
| 3 | danceability |
| 4 | speechiness |
| 5 | mode |
| 6 | instrumentalness |
| 7 | valence |
| 8 | key |
| 9 | duration_ms |
| 10 | acousticness |
| 11 | energy |
| 12 | tempo |
| 13 | liveness |
linearPoly = svm.SVC(kernel='poly', degree = 4)
linearPoly.kernel
modelSvm = linearPoly.fit(X_train, y_train)
predictionsPolyTest = linearPoly.predict(X_test)
predictionsPolyTrain = linearPoly.predict(X_train)
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning. "avoid this warning.", FutureWarning)
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsPolyTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsPolyTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 1.0 6218.0 870.0 0.0 0.0 0.0 0.0
1 0.0 12461.0 2444.0 0.0 0.0 0.0 0.0
2 0.0 10514.0 3527.0 0.0 0.0 0.0 0.0
3 0.0 5152.0 2169.0 0.0 0.0 0.0 0.0
4 0.0 1105.0 677.0 0.0 0.0 0.0 0.0
5 0.0 189.0 110.0 0.0 0.0 0.0 0.0
6 0.0 14.0 12.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 1.00 0.00 0.00 7089
1 0.35 0.84 0.49 14905
2 0.36 0.25 0.30 14041
3 0.00 0.00 0.00 7321
4 0.00 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 0.00 0.00 0.00 26
accuracy 0.35 45463
macro avg 0.24 0.16 0.11 45463
weighted avg 0.38 0.35 0.25 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsPolyTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsPolyTest))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 1539.0 223.0 0.0 0.0 0.0 0.0
1 0.0 3187.0 555.0 0.0 0.0 0.0 0.0
2 0.0 2586.0 843.0 0.0 0.0 0.0 0.0
3 0.0 1225.0 524.0 0.0 0.0 0.0 0.0
4 0.0 288.0 156.0 0.0 0.0 0.0 0.0
5 0.0 32.0 24.0 0.0 0.0 0.0 0.0
6 0.0 5.0 2.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 1762
1 0.36 0.85 0.51 3742
2 0.36 0.25 0.29 3429
3 0.00 0.00 0.00 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.36 11189
macro avg 0.10 0.16 0.11 11189
weighted avg 0.23 0.36 0.26 11189
linearRbf = svm.SVC(kernel='rbf')
linearRbf.kernel
modelSvm = linearRbf.fit(X_train, y_train)
predictionsRbvTest = linearRbf.predict(X_test)
predictionsRbvTrain = linearRbf.predict(X_train)
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning. "avoid this warning.", FutureWarning)
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsRbvTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsRbvTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 6125.0 964.0 0.0 0.0 0.0 0.0
1 0.0 12260.0 2645.0 0.0 0.0 0.0 0.0
2 0.0 10171.0 3870.0 0.0 0.0 0.0 0.0
3 0.0 4946.0 2375.0 0.0 0.0 0.0 0.0
4 0.0 1075.0 707.0 0.0 0.0 0.0 0.0
5 0.0 183.0 116.0 0.0 0.0 0.0 0.0
6 0.0 12.0 14.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 7089
1 0.35 0.82 0.49 14905
2 0.36 0.28 0.31 14041
3 0.00 0.00 0.00 7321
4 0.00 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 0.00 0.00 0.00 26
accuracy 0.35 45463
macro avg 0.10 0.16 0.12 45463
weighted avg 0.23 0.35 0.26 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsRbvTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsRbvTest))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 1521.0 241.0 0.0 0.0 0.0 0.0
1 0.0 3131.0 611.0 0.0 0.0 0.0 0.0
2 0.0 2494.0 935.0 0.0 0.0 0.0 0.0
3 0.0 1183.0 566.0 0.0 0.0 0.0 0.0
4 0.0 274.0 170.0 0.0 0.0 0.0 0.0
5 0.0 28.0 28.0 0.0 0.0 0.0 0.0
6 0.0 4.0 3.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 1762
1 0.36 0.84 0.51 3742
2 0.37 0.27 0.31 3429
3 0.00 0.00 0.00 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.36 11189
macro avg 0.10 0.16 0.12 11189
weighted avg 0.23 0.36 0.26 11189
linearSigmoid = svm.SVC(kernel='sigmoid')
linearSigmoid.kernel
modelSvm = linearSigmoid.fit(X_train, y_train)
predictionsSigmoidTest = linearSigmoid.predict(X_test)
predictionsSigmoidTrain = linearSigmoid.predict(X_train)
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning. "avoid this warning.", FutureWarning)
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsSigmoidTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsSigmoidTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 5328.0 1760.0 1.0 0.0 0.0 0.0
1 2.0 10881.0 4020.0 2.0 0.0 0.0 0.0
2 1.0 9492.0 4538.0 10.0 0.0 0.0 0.0
3 0.0 4814.0 2505.0 2.0 0.0 0.0 0.0
4 0.0 1144.0 638.0 0.0 0.0 0.0 0.0
5 0.0 173.0 126.0 0.0 0.0 0.0 0.0
6 0.0 13.0 13.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 7089
1 0.34 0.73 0.47 14905
2 0.33 0.32 0.33 14041
3 0.13 0.00 0.00 7321
4 0.00 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 0.00 0.00 0.00 26
accuracy 0.34 45463
macro avg 0.12 0.15 0.11 45463
weighted avg 0.24 0.34 0.25 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsSigmoidTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsSigmoidTest))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 1312.0 449.0 1.0 0.0 0.0 0.0
1 0.0 2769.0 973.0 0.0 0.0 0.0 0.0
2 0.0 2356.0 1071.0 2.0 0.0 0.0 0.0
3 0.0 1150.0 598.0 1.0 0.0 0.0 0.0
4 0.0 283.0 161.0 0.0 0.0 0.0 0.0
5 0.0 43.0 13.0 0.0 0.0 0.0 0.0
6 0.0 3.0 4.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 1762
1 0.35 0.74 0.48 3742
2 0.33 0.31 0.32 3429
3 0.25 0.00 0.00 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.34 11189
macro avg 0.13 0.15 0.11 11189
weighted avg 0.26 0.34 0.26 11189
linearRbf = svm.SVC(kernel='rbf')
linearRbf.kernel
modelSvm = linearRbf.fit(X_train, y_train)
predictionsRbvTest = linearRbf.predict(X_test)
predictionsRbvTrain = linearRbf.predict(X_train)
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning. "avoid this warning.", FutureWarning)
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsRbvTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsRbvTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 6125.0 964.0 0.0 0.0 0.0 0.0
1 0.0 12260.0 2645.0 0.0 0.0 0.0 0.0
2 0.0 10171.0 3870.0 0.0 0.0 0.0 0.0
3 0.0 4946.0 2375.0 0.0 0.0 0.0 0.0
4 0.0 1075.0 707.0 0.0 0.0 0.0 0.0
5 0.0 183.0 116.0 0.0 0.0 0.0 0.0
6 0.0 12.0 14.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 7089
1 0.35 0.82 0.49 14905
2 0.36 0.28 0.31 14041
3 0.00 0.00 0.00 7321
4 0.00 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 0.00 0.00 0.00 26
accuracy 0.35 45463
macro avg 0.10 0.16 0.12 45463
weighted avg 0.23 0.35 0.26 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsRbvTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsRbvTest))
Confusion matrix:
0 1 2 3 4 5 6
0 0.0 1521.0 241.0 0.0 0.0 0.0 0.0
1 0.0 3131.0 611.0 0.0 0.0 0.0 0.0
2 0.0 2494.0 935.0 0.0 0.0 0.0 0.0
3 0.0 1183.0 566.0 0.0 0.0 0.0 0.0
4 0.0 274.0 170.0 0.0 0.0 0.0 0.0
5 0.0 28.0 28.0 0.0 0.0 0.0 0.0
6 0.0 4.0 3.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.00 0.00 0.00 1762
1 0.36 0.84 0.51 3742
2 0.37 0.27 0.31 3429
3 0.00 0.00 0.00 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.36 11189
macro avg 0.10 0.16 0.12 11189
weighted avg 0.23 0.36 0.26 11189
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.

Bootstrap Aggregation (or Bagging for short), is a simple and very powerful ensemble method. An ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model.
Bootstrap Aggregation is a general procedure that can be used to reduce the variance for those algorithm that have high variance. An algorithm that has high variance are decision trees, like classification and regression trees. Decision trees are sensitive to the specific data on which they are trained. If the training data is changed (e.g. a tree is trained on a subset of the training data) the resulting decision tree can be quite different and in turn the predictions can be quite different.
randomForestModel = RandomForestClassifier()
randomForestModel.fit(X_train, y_train)
predictionsRandomForestTest = randomForestModel.predict(X_test)
predictionsRandomForestTrain = randomForestModel.predict(X_train)
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning)
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsRandomForestTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsRandomForestTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 7005.0 60.0 23.0 1.0 0.0 0.0 0.0
1 34.0 14816.0 39.0 15.0 0.0 1.0 0.0
2 24.0 160.0 13842.0 13.0 2.0 0.0 0.0
3 19.0 74.0 78.0 7149.0 1.0 0.0 0.0
4 2.0 16.0 29.0 11.0 1723.0 1.0 0.0
5 0.0 1.0 8.0 1.0 1.0 288.0 0.0
6 0.0 1.0 0.0 0.0 0.0 0.0 25.0
precision recall f1-score support
0 0.99 0.99 0.99 7089
1 0.98 0.99 0.99 14905
2 0.99 0.99 0.99 14041
3 0.99 0.98 0.99 7321
4 1.00 0.97 0.98 1782
5 0.99 0.96 0.98 299
6 1.00 0.96 0.98 26
accuracy 0.99 45463
macro avg 0.99 0.98 0.98 45463
weighted avg 0.99 0.99 0.99 45463
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsRandomForestTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsRandomForestTest))
Confusion matrix:
0 1 2 3 4 5 6
0 320.0 947.0 400.0 93.0 2.0 0.0 0.0
1 561.0 1929.0 1033.0 208.0 11.0 0.0 0.0
2 444.0 1479.0 1174.0 312.0 19.0 1.0 0.0
3 181.0 621.0 696.0 233.0 17.0 1.0 0.0
4 51.0 159.0 161.0 68.0 4.0 1.0 0.0
5 5.0 16.0 30.0 5.0 0.0 0.0 0.0
6 0.0 5.0 1.0 1.0 0.0 0.0 0.0
precision recall f1-score support
0 0.20 0.18 0.19 1762
1 0.37 0.52 0.43 3742
2 0.34 0.34 0.34 3429
3 0.25 0.13 0.17 1749
4 0.08 0.01 0.02 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.33 11189
macro avg 0.18 0.17 0.17 11189
weighted avg 0.30 0.33 0.31 11189
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
features = X_train.columns
#features = iris['feature_names']
importances = randomForestModel.feature_importances_
indices = np.argsort(importances)
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
lista = []
for i in indices:
lista.append(features[i])
featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
| Feature Importance | |
|---|---|
| 0 | duration_ms |
| 1 | danceability |
| 2 | loudness |
| 3 | tempo |
| 4 | energy |
| 5 | valence |
| 6 | acousticness |
| 7 | liveness |
| 8 | speechiness |
| 9 | instrumentalness |
| 10 | key |
| 11 | mode |
| 12 | time_signature |
| 13 | explicit |
XGBoost stands for “Extreme Gradient Boosting”.
XGBoost is used for supervised learning problems, where we use the training data (with multiple features) x's to predict a target variable y.
# fit model no training data
model = XGBClassifier(objective = 'multi:softmax')
model.fit(X_train, y_train)
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0,
learning_rate=0.1, max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
nthread=None, objective='multi:softprob', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1)
# make predictions for test data
y_predTest = model.predict(X_test)
predictionsTest = [round(value) for value in y_predTest]
# make predictions for test data
y_predTrain = model.predict(X_train)
predictionsTrain = [round(value) for value in y_predTrain]
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 121.0 5050.0 1868.0 50.0 0.0 0.0 0.0
1 30.0 10526.0 4219.0 130.0 0.0 0.0 0.0
2 8.0 6983.0 6803.0 246.0 1.0 0.0 0.0
3 1.0 2647.0 4206.0 467.0 0.0 0.0 0.0
4 0.0 447.0 1326.0 8.0 1.0 0.0 0.0
5 0.0 48.0 251.0 0.0 0.0 0.0 0.0
6 0.0 2.0 23.0 0.0 0.0 0.0 1.0
precision recall f1-score support
0 0.76 0.02 0.03 7089
1 0.41 0.71 0.52 14905
2 0.36 0.48 0.42 14041
3 0.52 0.06 0.11 7321
4 0.50 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 1.00 0.04 0.07 26
accuracy 0.39 45463
macro avg 0.51 0.19 0.17 45463
weighted avg 0.47 0.39 0.32 45463
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsTest))
Confusion matrix:
0 1 2 3 4 5 6
0 21.0 1356.0 372.0 13.0 0.0 0.0 0.0
1 13.0 2787.0 920.0 22.0 0.0 0.0 0.0
2 1.0 1977.0 1419.0 32.0 0.0 0.0 0.0
3 2.0 784.0 881.0 81.0 1.0 0.0 0.0
4 0.0 150.0 292.0 2.0 0.0 0.0 0.0
5 0.0 13.0 43.0 0.0 0.0 0.0 0.0
6 0.0 3.0 4.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.57 0.01 0.02 1762
1 0.39 0.74 0.52 3742
2 0.36 0.41 0.39 3429
3 0.54 0.05 0.09 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.39 11189
macro avg 0.27 0.17 0.14 11189
weighted avg 0.42 0.39 0.31 11189
xgb.plot_importance(model);
lista = []
for i in indices:
lista.append(features[i])
featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
| Feature Importance | |
|---|---|
| 0 | duration_ms |
| 1 | danceability |
| 2 | loudness |
| 3 | tempo |
| 4 | energy |
| 5 | valence |
| 6 | acousticness |
| 7 | liveness |
| 8 | speechiness |
| 9 | instrumentalness |
| 10 | key |
| 11 | mode |
| 12 | time_signature |
| 13 | explicit |
xgb.plot_tree(model, num_trees=2)
fig = plt.gcf()
fig.set_size_inches(150, 100)
fig.savefig('../images/tree2.png')
#xgb.to_graphviz(model, num_trees=2)
is a high-performance open source library for gradient boosting on decision trees. CatBoost has the flexibility of giving indices of categorical columns so that it can be encoded as one-hot encoding
CatBoost is used for supervised learning problems, where we use the training data (with multiple features) x's to predict a target variable y with multiple classes.
Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms.
train_data = X_train
eval_data = X_test
# cat_features = [0]
train_label = y_train
eval_label = y_test
# train_dataset = Pool(data=train_data,
# label=train_label,
# cat_features=cat_features)
train_dataset = Pool(data=train_data,
label=train_label)
eval_dataset = Pool(data=eval_data,
label=eval_label)
# eval_dataset = Pool(data=eval_data,
# label=eval_label,
# cat_features=cat_features)
# Initialize CatBoostClassifier
model = CatBoostClassifier(loss_function='MultiClass')
# Fit model
model.fit(train_dataset)
# Get predicted classes
preds_class = model.predict(eval_dataset)
# Get predicted probabilities for each class
preds_proba = model.predict_proba(eval_dataset)
# Get predicted RawFormulaVal
preds_raw = model.predict(eval_dataset,
prediction_type='RawFormulaVal')
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print("Confusion matrix:\n")
cm =confusion_matrix(train_label, model.predict(train_dataset))
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(train_label, model.predict(train_dataset)))
Confusion matrix:
0 1 2 3 4 5 6
0 412.0 4868.0 1719.0 89.0 1.0 0.0 0.0
1 70.0 10932.0 3707.0 196.0 0.0 0.0 0.0
2 27.0 5977.0 7698.0 335.0 4.0 0.0 0.0
3 9.0 2384.0 3826.0 1099.0 3.0 0.0 0.0
4 1.0 419.0 1194.0 139.0 29.0 0.0 0.0
5 0.0 45.0 205.0 41.0 7.0 1.0 0.0
6 0.0 1.0 23.0 2.0 0.0 0.0 0.0
precision recall f1-score support
0 0.79 0.06 0.11 7089
1 0.44 0.73 0.55 14905
2 0.42 0.55 0.47 14041
3 0.58 0.15 0.24 7321
4 0.66 0.02 0.03 1782
5 1.00 0.00 0.01 299
6 0.00 0.00 0.00 26
accuracy 0.44 45463
macro avg 0.56 0.22 0.20 45463
weighted avg 0.52 0.44 0.38 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(eval_label, preds_class)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(eval_label, preds_class))
Confusion matrix:
0 1 2 3 4 5 6
0 39.0 1239.0 460.0 22.0 2.0 0.0 0.0
1 23.0 2490.0 1166.0 62.0 1.0 0.0 0.0
2 8.0 1683.0 1595.0 139.0 4.0 0.0 0.0
3 4.0 620.0 949.0 171.0 5.0 0.0 0.0
4 0.0 108.0 300.0 35.0 1.0 0.0 0.0
5 0.0 5.0 46.0 5.0 0.0 0.0 0.0
6 0.0 1.0 4.0 2.0 0.0 0.0 0.0
precision recall f1-score support
0 0.53 0.02 0.04 1762
1 0.41 0.67 0.50 3742
2 0.35 0.47 0.40 3429
3 0.39 0.10 0.16 1749
4 0.08 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.38 11189
macro avg 0.25 0.18 0.16 11189
weighted avg 0.39 0.38 0.32 11189
model.get_feature_importance(prettified= True)
| Feature Id | Importances | |
|---|---|---|
| 0 | danceability | 11.047897 |
| 1 | duration_ms | 11.005506 |
| 2 | speechiness | 9.240048 |
| 3 | loudness | 8.881768 |
| 4 | energy | 8.687099 |
| 5 | valence | 8.268764 |
| 6 | acousticness | 8.190099 |
| 7 | instrumentalness | 8.180610 |
| 8 | tempo | 7.736335 |
| 9 | liveness | 7.405509 |
| 10 | key | 4.735856 |
| 11 | explicit | 3.432434 |
| 12 | mode | 2.325851 |
| 13 | time_signature | 0.862224 |
NGBoost stands for “Natural Gradient Boosting”.
Gradient Boosting methods have generally been among the top performers in predictive accuracy over structured or tabular input data. NGBoost enables predictive uncertainty estimation with Gradient Boosting through probabilistic predictions (including real valued outputs). With the use of Natural Gradients, NGBoost overcomes technical challenges that make generic probabilistic prediction hard with gradient boosting.
from ngboost import NGBClassifier
from ngboost.distns import k_categorical
from ngboost.learners import default_tree_learner
from ngboost.scores import MLE
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
# X, Y = load_boston(True)
# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
ngb = NGBClassifier(Dist=k_categorical(len(labels)))
ngb.fit(X_train, y_train) # Y should have only 3 values: {0,1,2}
/Applications/anaconda3/lib/python3.7/site-packages/ngboost/distns/categorical.py:47: RuntimeWarning: divide by zero encountered in log return -np.log(self.probs[Y, range(len(Y))])
[iter 0] loss=1.4766 val_loss=0.0000 scale=0.5000 norm=4.2247
/Applications/anaconda3/lib/python3.7/site-packages/ngboost/distns/categorical.py:47: RuntimeWarning: divide by zero encountered in log return -np.log(self.probs[Y, range(len(Y))])
[iter 100] loss=1.4322 val_loss=0.0000 scale=1.0000 norm=7.0090 [iter 200] loss=1.4129 val_loss=0.0000 scale=1.0000 norm=6.8939 [iter 300] loss=1.4044 val_loss=0.0000 scale=1.0000 norm=6.8734 [iter 400] loss=1.3995 val_loss=0.0000 scale=0.5000 norm=3.4414
NGBClassifier(Base=DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False, random_state=None,
splitter='best'),
Dist=<class 'ngboost.distns.categorical.k_categorical.<locals>.Categorical'>,
Score=<class 'ngboost.scores.MLE'>, learning_rate=0.01,
minibatch_frac=1.0, n_estimators=500, natural_gradient=True,
tol=0.0001, verbose=True, verbose_eval=100)
# make predictions for test data
y_predTest = ngb.predict(X_test)
predictionsTest = [round(value) for value in y_predTest]
# make predictions for test data
y_predTrain = ngb.predict(X_train)
predictionsTrain = [round(value) for value in y_predTrain]
print("Confusion matrix:\n")
cm =confusion_matrix(y_train, predictionsTrain)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_train, predictionsTrain))
Confusion matrix:
0 1 2 3 4 5 6
0 87.0 5182.0 1781.0 36.0 3.0 0.0 0.0
1 10.0 10680.0 4106.0 109.0 0.0 0.0 0.0
2 0.0 7753.0 6091.0 197.0 0.0 0.0 0.0
3 0.0 3209.0 3772.0 340.0 0.0 0.0 0.0
4 0.0 632.0 1138.0 12.0 0.0 0.0 0.0
5 0.0 88.0 209.0 2.0 0.0 0.0 0.0
6 0.0 4.0 21.0 0.0 0.0 0.0 1.0
precision recall f1-score support
0 0.90 0.01 0.02 7089
1 0.39 0.72 0.50 14905
2 0.36 0.43 0.39 14041
3 0.49 0.05 0.08 7321
4 0.00 0.00 0.00 1782
5 0.00 0.00 0.00 299
6 1.00 0.04 0.07 26
accuracy 0.38 45463
macro avg 0.45 0.18 0.15 45463
weighted avg 0.46 0.38 0.30 45463
/Applications/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. 'precision', 'predicted', average, warn_for)
print("Confusion matrix:\n")
cm =confusion_matrix(y_test, predictionsTest)
print_cm(cm, labelsMatrix, hide_zeroes=False, hide_diagonal=False, hide_threshold=None)
print("\n")
print(classification_report(y_test, predictionsTest))
Confusion matrix:
0 1 2 3 4 5 6
0 17.0 1418.0 318.0 9.0 0.0 0.0 0.0
1 5.0 2961.0 758.0 18.0 0.0 0.0 0.0
2 0.0 2246.0 1156.0 27.0 0.0 0.0 0.0
3 0.0 980.0 712.0 57.0 0.0 0.0 0.0
4 0.0 225.0 219.0 0.0 0.0 0.0 0.0
5 0.0 18.0 37.0 1.0 0.0 0.0 0.0
6 0.0 1.0 6.0 0.0 0.0 0.0 0.0
precision recall f1-score support
0 0.77 0.01 0.02 1762
1 0.38 0.79 0.51 3742
2 0.36 0.34 0.35 3429
3 0.51 0.03 0.06 1749
4 0.00 0.00 0.00 444
5 0.00 0.00 0.00 56
6 0.00 0.00 0.00 7
accuracy 0.37 11189
macro avg 0.29 0.17 0.13 11189
weighted avg 0.44 0.37 0.29 11189
lista = []
for i in indices:
lista.append(features[i])
featureImportancedf = pd.DataFrame( lista[::-1])
featureImportancedf.columns = ['Feature Importance']
featureImportancedf
| Feature Importance | |
|---|---|
| 0 | duration_ms |
| 1 | danceability |
| 2 | loudness |
| 3 | tempo |
| 4 | energy |
| 5 | valence |
| 6 | acousticness |
| 7 | liveness |
| 8 | speechiness |
| 9 | instrumentalness |
| 10 | key |
| 11 | mode |
| 12 | time_signature |
| 13 | explicit |
Based on the proposed scores, the best model to predict the test set was the CatBoost Model; NGBoost had the best precision, but in accuracy, recall and f1-score, the best model is CatBoost. Because the classification is not dichotomous but multiclass, there is no graphical representation of the ROC curve due to dimensionality; In addition, it is not so easy to relate prediction metrics (for example, accuracy) to an established benchmark, the best benchmark for comparison is that it has 100% successes in some class and zero in the others (which on average equals a distribution uniform data by class) which in this case corresponds to 10% (due to the ten classes) [16.6% for 6 classes]
| Accuracy | precision | recall | f1-score | |
|---|---|---|---|---|
| Logistic Regression | 0.36 | 0.31 | 0.36 | 0.27 |
| SVM | 0.35 | 0.22 | 0.35 | 0.25 |
| SVM Polynomial | 0.37 | 0.42 | 0.37 | 0.29 |
| SVM RBF | 0.37 | 0.41 | 0.37 | 0.29 |
| SVM Sigmoid | 0.26 | 0.27 | 0.26 | 0.26 |
| Random Forest | 0.35 | 0.34 | 0.35 | 0.3 |
| XGBoost | 0.37 | 0.41 | 0.37 | 0.29 |
| CatBoost | 0.38 | 0.4 | 0.38 | 0.31 |
| NGBoost | 0.37 | 0.43 | 0.37 | 0.28 |
Looking at the feature importance for the models, every model had different features as the main characteristics to decide the classification, but some interesting conclusions arose from this initial approach.
| Feature Importance | Logistic Regression | SVM | Random Forest | XGBoost | CatBoost | NGBoost |
|---|---|---|---|---|---|---|
| 1 | danceability | loudness | duration_ms | duration_ms | duration_ms | duration_ms |
| 2 | instrumentalness | valence | loudness | loudness | danceability | loudness |
| 3 | explicit | time_signature | danceability | danceability | loudness | danceability |
| 4 | energy | explicit | valence | valence | energy | valence |
| 5 | valence | acousticness | acousticness | acousticness | speechiness | acousticness |
| 6 | mode | duration_ms | speechiness | speechiness | valence | speechiness |
| 7 | liveness | key | tempo | tempo | liveness | tempo |
| 8 | acousticness | instrumentalness | energy | energy | acousticness | energy |
| 9 | speechiness | danceability | liveness | liveness | instrumentalness | liveness |
| 10 | duration_ms | mode | instrumentalness | instrumentalness | tempo | instrumentalness |
| 11 | time_signature | tempo | key | key | key | key |
| 12 | tempo | speechiness | mode | mode | explicit | mode |
| 13 | loudness | energy | time_signature | time_signature | mode | time_signature |
| 14 | key | liveness | explicit | explicit | time_signature | explicit |
XGBOOST and Random Forest models selected a group of features as the most important; duration_ms, loudness, danceability, valence, acousticness, among other. Every feature for these models is related somehow to the rest of the features; happening in both models. On the other hand, the SVM Linear Kernel and the Logistic Regression (both linear) selected a group of features as the most important as well; explicit is the main feature diference between the two groups of models. The only variable shared by the groups is danceability. Apparently, there are two approaches for this classification problem: a linear approach and a non-linear; only by looking at the features each model selects.
Therefore, following what the best model says, if you want a song to reach the maximum level of popularity you must take into account the duration of the song; how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity; the energy it produces, how fast, loud and noisy it is; and level of decibels.
https://spotipy.readthedocs.io/en/2.7.0/
https://developer.spotify.com/documentation/web-api/
https://developer.spotify.com/documentation/web-api/reference/tracks/get-several-tracks/
https://en.wikipedia.org/wiki/Precision_and_recall
https://stats.stackexchange.com/questions/367155/why-lasso-for-feature-selection
https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148
https://towardsdatascience.com/kernel-function-6f1d2be6091
https://machinelearningmastery.com/develop-first-xgboost-model-python-scikit-learn/
https://help.bitbond.com/article/20-the-10-loan-status-variants-explained
https://xgboost.readthedocs.io/en/latest/parameter.html#parameters-for-tree-booster
https://www.geeksforgeeks.org/svm-hyperparameter-tuning-using-gridsearchcv-ml/
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
https://scikit-learn.org/stable/modules/svm.html
https://xgboost.readthedocs.io/en/latest/
https://xgboost.readthedocs.io/en/latest/parameter.html
https://github.com/stanfordmlgroup/ngboost/tree/master/examples
https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db
